Dynamic and cryptographically secure augmentation of programmatically established chatbot sessions

ABSTRACT

The disclosed exemplary embodiments include computer-implemented systems, apparatuses, and processes that dynamically and securely augment a programmatically established communications session, such as a chatbot session, to include one or more additional responsive applications. For example, an apparatus may receive messaging data during a first communication session programmatically established between a device and a first executed application program, and may determine that an additional apparatus is configured to perform operations consistent with the messaging data. The apparatus may transmit a digital token and at least a portion of the messaging data to an additional apparatus. A second application executed by the additional apparatus may validate the digital token and based on the portion of the messaging data, establish a second communication session between the device and the executed first and second application programs.

TECHNICAL FIELD

The disclosed embodiments generally relate to computer-implementedsystems and processes that dynamically and securely augmentprogrammatically established chatbot sessions

BACKGROUND

Many institutions, online retailers, and other business use chatbots toincrease and improve a level of user engagement within correspondingdigital platforms, such as, but not limited to, websites, messagingapplications, and mobile applications. These existing chatbots mayreceive a message from a user's device (e.g., provided as input to acorresponding chat interface), programmatically generate responses tothese received messages, and generate and transmit, to the user'sdevice, a response to the received message for presentation within acorresponding digital interface.

SUMMARY

In some examples, an apparatus includes a communications unit, a memorystoring instructions, and at least one processor coupled to thecommunications unit and to the memory. The at least one processor isconfigured to execute the instructions to receive, via thecommunications unit, a first signal from a device that includesmessaging data. The first signal may be received during a firstcommunication session established between the device and a firstexecuted application program. The at least one processor may be furtherconfigured to execute the instructions to determine that an additionalapparatus is configured to perform operations consistent with themessaging data, and generate and transmit, via the communications unit,a second signal to the additional apparatus that includes a digitaltoken and at least a portion of the messaging data. The second signalmay include information that causes a second application programexecuted by the additional apparatus to validate the digital token, andestablish a second communication session between the device and theexecuted first and second application programs based on the portion ofthe messaging data.

In other examples, a computer-implemented method includes receiving,using at least one processor, a first signal from a device that includesmessaging data. The first signal may be received during a communicationsession established between the device and a first executed applicationprogram. The computer-implemented method also includes determining,using the at least one processor, that an additional apparatus isconfigured to perform operations consistent with the messaging data andusing the at least one processor, generating and transmitting a secondsignal to the additional apparatus that includes a digital token and atleast a portion of the messaging data. The second signal may includeinformation that causes a second application program executed by theadditional apparatus to validate the digital token and establish asecond communication session between the device and the executed firstand second application programs based on the portion of the messagingdata.

Further, in some examples, an apparatus includes a communications unit,a memory storing instructions, and at least one processor coupled to thecommunications unit and to the memory. The at least one processor isconfigured to execute the instructions to receive, via thecommunications unit, a first signal that includes session data and adigital token. The session data may characterize a first communicationssession between a device and a first executed application program. Theat least one processor is further configured to execute the instructionsto perform operations that validate the digital token and based on thevalidation of the digital token, establish a second communicationsession between the device, the first executed application program, anda second application program executed by the apparatus. The secondcommunications session may be established in accordance with a portionof the session data. Further, the at least one processor is configuredto execute the instructions to generate and transmit, via thecommunications unit, a second signal that includes interface dataidentifying the second communications session. The second signal mayinclude information that causes the device to present the interface datawithin a portion of a digital interface associated with the firstcommunications session.

The details of one or more exemplary embodiments of the subject matterdescribed in this specification are set forth in the accompanyingdrawings and the description below. Other potential features, aspects,and advantages of the subject matter will become apparent from thedescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C and 2A-2C are block diagrams illustrating portions of anexemplary computing environment, in accordance with some exemplaryembodiments.

FIGS. 3 and 4 are flowcharts of an exemplary processes for a dynamicallyaugmenting a programmatically generated chatbot session, in accordancewith some exemplary embodiments.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIGS. 1A-1C illustrate components of an exemplary computing environment100, which perform computerized processes that establish acommunications session between a device and a responsive application,and based on natural language queries generated by the device, thataugment or supplement the established communications session to includeone or more additional responsive applications, in accordance with someexemplary implementations. By way of example, and referring to FIG. 1A,environment 100 includes a client device 102, such as a smart phone,tablet computer, wearable device, or other computing device associatedwith a user 101, a first computing system 122, and a second computingsystem 152, each of which may be interconnected through one or morecommunications networks, such as communications network 120. Examples ofcommunications network 120 include, but are not limited to, a wirelesslocal area network (LAN), e.g., a “Wi-Fi” network, a network utilizingradio-frequency (RF) communication protocols, a Near Field Communication(NFC) network, a wireless Metropolitan Area Network (MAN) connectingmultiple wireless LANs, and a wide area network (WAN), e.g., theInternet.

In some instances, client device 102 may include one or more tangible,non-transitory memories that store data and/or software instructions andone or more processors configured to execute the software instructions.The stored software instructions may, for example, include one or moreapplication programs, one or more application modules, or other elementsof code executable by the one or more processors. For instance, and asillustrated in FIG. 1A, client device 102 may store, within the one ormore tangible, non-transitory memories, a chatbot application 104 that,when executed by the one or more processors, causes client device 102 toestablish programmatically a communications session (hereinafter, a“chatbot” session) with an application program executed by one or moreof first computing system 122 or second computing system 152, and topresent message data exchanges during the established chatbot sessionwithin a portion of a digital interface (hereinafter, a “chatbotinterface”). Examples of chatbot application 104 may include, but arenot limited to, a web browser (e.g., Google Chrome™, Apple Safari™,etc.), a payment or banking application associated with a financialinstitution, or other executable applications that generate dedicatedchatbot interfaces.

Client device 102 may also include a communications unit, such as one ormore wireless transceivers, coupled to the one or more processors foraccommodating wired or wireless internet communication with firstcomputing system 122 or second computing system 152. Further, clientdevice 102 may also include a display unit coupled to the one or moreprocessors and configured to present interface elements to user 101, andone or more input units coupled to the one or more processors andconfigured to receive input from user 101, e.g., in response tomessaging data rendered for presentation within the chatbot interface.By way of example, the display unit may include, but is not limited to,an LCD display, a TFT display, and OLED display, or other appropriatetype of display unit, and one or more input units may include, but arenot limited to, a keypad, keyboard, touchscreen, fingerprint scanner,microphone, voice activated control technology, stylus, or any otherappropriate type of input unit. Further, in some examples, thefunctionalities of the display and input units may be combined into asingle device, such as a pressure-sensitive touchscreen display unitthat can present interface elements and can detect an input from user101 via a physical touch.

As described herein, each of first computing system 122 and secondcomputing system 152 may represent a computing system that includes oneor more servers and tangible, non-transitory memory devices storingexecutable code and application modules. Further, the one or moreservers may each include one or more processors, which may be configuredto execute portions of the stored code or application modules to performoperations consistent with the disclosed embodiments. In some instances,first computing system 122 or second computing system 152 may can beincorporated into a single computing system, although in otherinstances, first computing system 122 or second computing system 152 cancorrespond to a distributed system that includes computing componentsdistributed across communications network 120, such as those describedherein, or those provided or maintained by cloud-service providers(e.g., Google Cloud™, Microsoft Azure™, etc.). The disclosed embodimentsare, however, not limited to these exemplary distributed systems, and inother instances, first computing system 122 or second computing system152 may include computing components disposed within any additional oralternate number or type of computing systems or across any appropriatenetwork.

In one instance, first computing system 122 and additionally, oralternatively, second computing system 152 may be associated with, oroperated by, a financial institution or other business entity thatprovides financial services to one or more customers, such as user 101.Examples of these financial services include, but are not limited to,establishing and maintaining a financial services account on behalf of acorresponding customer (e.g., a deposit account, a brokerage account, acredit card account or a revolving line of credit, etc.), issuing aninsurance policy to a corresponding customer (e.g., term or whole lifeinsurance policies, vehicle insurance policies, etc.), initiating apayment transaction involving a corresponding customer, initiating asale or purchase of securities or on behalf of a corresponding customer,or servicing inquiries related to any of the financial servicesdescribed herein. Further, in some instances, first computing system 122and second computing system 152 may be associated with a singlefinancial institution or business entity (e.g., represent differentbusiness units within a common financial institution). On otherinstances, and as described herein, first computing system 122 andsecond computing system 152 may be associated with different financialinstitutions or business entities.

As described herein, one or more of the application programs executed byclient device 102, such as chatbot application 104, may performoperations that programmatically establish a communications session,e.g., a chatbot session, with one or more application programs executedby first computing system 122, such as first chatbot engine 124 inFIG. 1. For example, user 101 may provide input to client device 102,e.g., via the input unit, that requests an execution of chatbotapplication 104, and upon execution, chatbot application 104 may performoperations that generate and render one or more interface elements forpresentation on a corresponding authentication interface, e.g., via acorresponding display unit (not illustrated in FIG. 1A). In someexamples, the authentication interface may include interface elementsthat prompt user 101 to provide, via the input unit, input thatspecifies one or more authentication credentials, such as, but notlimited to, an alphanumeric login credential, an alphanumeric password,or a biometric credential (e.g., a fingerprint scan, a facial image,etc.).

Based on the provided authentication credentials, executed chatbotapplication 104 may perform operations that authenticate an identity ofuser 101 based on locally maintained authentication data and/or based oninformation exchanged with first computing system 122 through aprogrammatic interface, such as application programming interface (API)128. Responsive to a successful authentication of the identity of user101, executed chatbot application 104 may perform additional operationsthat generate a request to initiate a chatbot session with first chatbotengine 124. The request may include, but is not limited to, dataconfirming a successful authentication of the identity of user 101(e.g., an application cryptogram having a predetermined format), anidentifier of user 101 (e.g., the login or biometric credential, etc.),and/or an identifier of client device 102 (e.g., an IP or MAC address),and client device 102 may transmit the generated request acrosscommunications network 120 to first computing system 122, e.g., usingany appropriate communications protocol.

API 128 may receive the request, and upon execution by first computingsystem 122, first chatbot engine 124 may perform operations that verifythe authenticity of the request and confirm the authentication of theidentity of user 101 by client device 102, e.g., based on theconfirmation data, the user identifier, and/or the device identifier.Based on a successful verification and confirmation, first chatbotengine 124 may perform operations that initiate the chatbot session withchatbot application 104, e.g., based on an established handshake betweenAPI 128 and the communications unit of client device 102 (notillustrated in FIG. 1A). First chatbot engine 124 may perform additionaloperations that generate an initial, introductory message for theestablished chatbot session based on, among other things, one or morepredetermined rules that specify appropriate introductory messages(e.g., as maintained locally within the one or more tangible,non-transitory memories, such as data repository 126), a time or date atwhich first chatbot engine 124 established the chatbot session , andadditionally, or alternatively, the user or device identifiers.

For example, the introductory message may include textual content thatincludes a greeting and that prompts user 101 to further interact withthe established chatbot session (e.g., “Good morning! How can we helpyou?), and first chatbot engine 124 may perform operations that generateintroductory message data specifying the introductory message. Further,first chatbot engine 124 may perform additional operations that generateone or more elements of session data identifying and characterizing theestablished chatbot session, and store the generated elements within aportion of the one or more tangible, non-transitory memories, e.g.,within data records of session database 126A of data repository 126. Thegenerated session data may include, but is not limited to, analphanumeric session identifier 176, the user identifier and the deviceidentifier (and in some instances, the confirmation data), a time ordate at which first chatbot engine 124 initiated the chatbot session,and/or data characterizing the introductory message. In some instances,and as described herein, the data records of session database 126A mayestablish a time-evolving record of the messages exchanged between firstchatbot engine 124 and executed chatbot application 104 during theestablished chatbot session.

In some instances, executed first chatbot engine 124 may performoperations that cause first computing system 122 to transmit, acrosscommunications network 120, the introductory message data andinformation identifying the established chatbot session, such as, butnot limited to, session identifier 176, to client device 102. A secureprogrammatic interface associated with executed chatbot application 104,such as application programming interface (API) 105, may receive theintroductory message data and session identifier 176, and executedchatbot application 104 may perform operations that store theintroductory message data and session identifier 176 within one or moretangible, non-transitory memories.

Responsive to the receipt of the introductory message data, executedchatbot application 104 may generate, and render for presentation, anadditional digital interface, e.g., a chatbot interface 182, thatincludes the introductory message data and facilitates an ongoing andsimulated conversation between user 101 and a chatbot generated orhosted programmatically by first chatbot engine 124 of first computingsystem 122. In some instances, chatbot application 104 may performoperations that generate, and render for presentation, all or a portionof the introductory message data (e.g., “Good Morning! How can we help?)for presentation within chatbot interface 182, e.g., as introductorymessage 190. The automatic presentation of introductory message 190 maysimulate a conversation between user 101 and the programmatic chatbotmaintained by first computing system 122 and as illustrated in FIG. 1A,introductory message 190 greets user 101 and prompts user 101 to furtherinteract with the established chatbot session.

As described herein, first computing system 122 may be associated with,or operated by, a financial institution that provides one or morefinancial services to various customers, such as user 101. For example,first computing system 122 may be associated with a particular businessunit of the financial institution that provides a subset of thefinancial services to user 101, such as, but not limited to, a retailbanking unit that establishes, maintains, and provisions one or moredeposit accounts, debit card accounts, credit card accounts, or lines ofcredit to user 101. Via chatbot interface 182, user 101 may interactwith the established chatbot session to resolve inquiries associatedwith the subset of the financial services, such as, but not limited to,a request for a current balance of a checking account or a credit cardaccount issued by the financial institution, a request to initiatebill-payment transaction involving the checking account, a request toreplace a physical transaction card associated with a credit or debitcard account issued by the financial institution, or a request thatfirst computing system 122 provision client device 102 with one or moreapplication programs (e.g., mobile payment or banking applications). Insome instances, and based on messaging data transmitted programmaticallyto first computing system 122 by chatbot application 104, first chatbotengine 124 may perform any of the exemplary processes described hereinto parse the receive message data to identify an underlying inquiry, togenerate textual content in response to the underlying query based onlocally available data, e.g., as maintained within data repository 126,and to transmit additional messaging data that includes the textualcontent to client device 102, e.g., for presentation within chatbotinterface 182.

In other instances, the established chatbot session may involve aninquiry unrelated to those services provided to user 101 by the retailbanking unit of the financial institution, and as such, first chatbotengine 124 may be incapable of generating programmatically a response tothat underlying inquiry based on elements of sensitive customer,account, or transaction maintained by first computing system 122. Forexample, input data provided by user 101 to chatbot interface 182 (e.g.,via the input unit of client device 102), and messaging data generatedby chatbot application 104, may include an inquiry referencing a productor service provided by another business unit of the financialinstitution (e.g., an insurance product, a sale or a purchase of asecurity) or an inquiry related to a product or service provided by anadditional, unrelated financial institution (e.g., an inquiry regardingan account balance of an account held at a different financialinstitution).

Based on a receipt of messaging data that includes such an unrelatedinquiry, executed first chatbot engine 124 may perform any of theexemplary processes described herein to identify an additional computingsystem that operates within environment 100 and is configured to, orcapable of, accessing data responsive to each of the unrelated inquiryand further, to augment dynamically the established chatbot session toinclude another chatbot engine that, when executed by the additionalcomputing system, generates a response to the unrelated inquiry andtransmits the additional messaging data to client device 102 forpresentation within chatbot interface 182. In some examples, the dynamicaugmentation of the established chatbot session may facilitate anongoing and simulated conversation between user 101 and the chatbotprogrammatically generated by first chatbot engine 124 (e.g., asexecuted by first computing system 122) and by the additional computingsystem.

Further, certain of these exemplary processes, as described herein,provision a single digital interface, e.g., chatbot interface 182, withmessage data responsive to user inquires related to, and unrelated to,the financial services offered to user 101 by the financial institutionoperated by or associated with first chatbot engine 124, and can beimplemented in addition to, or as an alternate to, conventionalprocesses that, upon detection of the unrelated inquiry by first chatbotengine 124, generate predetermined messaging data indicative of aninability of first chatbot engine 124 to resolve the inquiry, orprovision to chatbot interface 182 additional content (e.g., ahyperlink, deep link, etc.) identifying resources capable of resolvingthe unrelated inquiry. In some instances, an implementation of certainof the exemplary processes described herein, which generate and presentmessaging data response to related and unrelated inquiries within asingle digital interface, may improve an ability of user 101 to interactwith the augmented chatbot sessions using communications devices havinglimited size, display, or input functionalities, e.g., smart phones,smart watches, or other wearable form factors characterized by a limitedability to parse through, and interact with, additional linkedinterfaces disposed across multiple display screens.

Referring back to FIG. 1A, and responsive to introductory message 190,user 101 may provide additional input 106 to chatbot interface 182 viathe input unit of client device 102. In some instances, additional input106 may include textual input provided using a miniaturized “virtual”keyboard presented within chatbot interface 182 by a pressure-sensitive,touchscreen display unit of client device 102, or using a keypad orkeyboard included within, or coupled to client device 102. In otherinstances, additional input 106 may include an audible utteranceprovided to a microphone included within, or coupled to, client device102.

In some instances, additional input 106 may include an inquiry unrelatedto the products or services provided to user 101 by the retail bankingunit, such as, but not limited to, a current value of a brokerage orinvestment account maintained by an investment banking unit of thefinancial institution. As illustrated in FIG. 1A, executed chatbotapplication 104 may receive additional input 106, and may performoperations that render additional input 106 for presentation as anadditional message 192 within chatbot interface 182, e.g., “What's thecurrent balance in my brokerage account?”. Further, when additionalinput 106 includes audio content, e.g., the spoken utterance, chatbotapplication 104 may perform operations (not illustrated in FIG. 1A) thatconvert or transform the audio content into textual contentrepresentative of the spoken utterance based on an application of one ormore speech recognition algorithms to all, or to selected portions, ofthe audio content.

Chatbot application 104 may perform operations that package all, or aselected portion, of additional input 106 into corresponding portions ofmessaging data 108. In some instances, messaging data 108 may includetextual content that specifies additional message 192 (e.g., “What's thecurrent balance of my investment account?”), along with sessionidentifier 176 and temporal data characterizing a time or date at whichuser 101 provided additional input 106 to client device 102.Additionally, or alternatively, messaging data 108 may also include aunique identifier of user 101 (e.g., a login credential, a digitalidentifier, etc.), a unique identifier of client device 102 (e.g., an IPaddress, a MAC address, etc.), and/or an application cryptogramgenerated by executed chatbot application 104. Chatbot application 104may perform additional operations that cause client device 102 totransmit messaging data 108 across communications network 120 to firstcomputing system 122, e.g., using any appropriate communicationsprotocol.

First computing system 122 may receive messaging data 108 through asecure programmatic interface, such API 128 of first chatbot engine 124.In some instances, not illustrated in FIG. 1A, first chatbot engine 124may perform operations that verify messaging data 108 is associatedwith, and was generated during the established chatbot session withchatbot application 104. For example, first chatbot engine 124 may parsemessaging data 108 and extract session identifier 176, the useridentifier of user 101, the device identifier of client device 102,and/or the application cryptogram associated with executed chatbotapplication 104. Executed first chatbot engine 124 may also accesslocally maintained copies of the session identifier, the useridentifier, the device identifier, and/or the application cryptogramassociated with the established chatbot session, e.g., as stored withinthe data records of session database 126A, and perform operations thatverify a correspondence between the extracted and locally accessiblecopies of the session identifier, the user identifier, the deviceidentifier, and/or the application cryptogram.

If, for example, executed first chatbot engine 124 were determine aninconsistency between one or more of the extracted and locallyaccessible copies of the session identifier, the user identifier, thedevice identifier, and/or the application cryptogram, first chatbotengine 124 may discard received messaging data 108, terminate theestablished chatbot session, and generate and transmit and error messageto client device 102, e.g., through API 128 (not illustrated in FIG.1A). Alternatively, and response to an established consistency betweenthe extracted and locally accessible copies of the session identifier,the user identifier, the device identifier, and/or the applicationcryptogram, executed first chatbot engine 124 may perform operationsthat associate received messaging data 108 with the established chatbotsession and store received messaging data 108 within a correspondingportion of session database 126A. In some instances, executed firstchatbot engine 124 may also perform any of the exemplary processesdescribed herein to identify and characterize one or more inquiriesspecifies within messaging data 108 (and one or more products orservices associated with the unrelated inquiry), to identify anadditional computing system capable of accessing data responsive theinquiry and further, to augment the established chatbot session toinclude an another chatbot engine that, when executed by that identifiedcomputing system, generates a messaging data responsive to the unrelatedinquiry.

Referring to FIG. 1B, received messaging data 108 may be routed to asession management module 130 of executed first chatbot engine 124,which may perform operations that parse messaging data 108 to extracttextual data 110 (e.g., representative of additional message 192,including “What's the current balance of my investment account?”). Insome instances, session management module 130 may generate aprogrammatic command that executes a natural language processing (NLP)engine 132 of first chatbot engine 124, and as illustrated in FIG. 1B,session management module 130 may provide extracted textual data 110 asan input to executed NLP engine 132. In other instances, consistent withthe disclosed exemplary embodiments, a functionality of sessionmanagement module 130 may be performed by executed NLP engine 132, andAPI 128 may route received messaging data 108 directly to executed NLPengine 132, which may process messaging data 108 and extract textualdata 110.

NLP engine 132 may receive textual data 110, and may apply one or morenatural language processing (NLP) algorithms or techniques to all or aportion of textual data 110. Based on the application of these naturallanguage processing algorithms, NLP engine 132 may identify one or morediscrete linguistic elements (e.g., a word, a combination of morphemes,a single morpheme, etc.) within textual data 110, and may establish acontext and a meaning of combinations of the discrete linguisticelements, e.g., based on the identified discrete linguistic elements,relationships between these discrete linguistic elements, and relativepositions of these discrete linguistic elements within textual data 110.In some instances, NLP engine 132 may generate output data 112 thatinclude linguistic element data 112A and contextual information 112B.

As described herein, linguistic element data 112A each of the discretelinguistic elements, and contextual information 112B that specifies theestablished context or meaning of the combination of the discretelinguistic elements. The established context or meaning of thecombination of the discrete linguistic elements may, for example,identify a product or service associated with or requested by additionalmessage 192, e.g., as specified by the unrelated inquiry within textualdata 110, and additionally, or alternatively, may identify a particulardata type or data class that would be responsive to that inquiry. Asdescribed herein, textual data 110 may include an inquiry related to aparticular product (e.g., a credit card account, an investment account,an insurance policy, etc.), a request for a performance of a particularservice (e.g., providing an account balance, etc.), or a request for aparticular mobile application to be provisioned to client device 102. Insome instances, and based on the application of the natural languageprocessing techniques, executed NLP engine 132 may generate contextualinformation 112B that characterizes the inquiry associated withadditional message 192, the product or service specified by thatinquiry, and further, specific types or classes of data, if accessibleto first chatbot engine 124, would facilitate a generation of a responseto the inquiry (e.g., “responsive” data types or classes).

Examples of these NLP algorithms or techniques may include one or moremachine learning processes, such as, but not limited to, a clusteringalgorithm or unsupervised learning algorithm (e.g., a k-means algorithm,a mixture model, a hierarchical clustering algorithm, etc.), asemi-supervised learning algorithm, or a decision-tree algorithm. Inother examples, the NLP algorithms or techniques may also include one ormore artificial intelligence models, such as, but not limited to, anartificial neural network model, a recurrent neural network model, aBayesian network model, or a Markov model. Further, the NLP algorithmsor techniques may also include one or more statistical processes, suchas those that make probabilistic decisions based on attachingreal-valued weights to elements of certain input data.

By way of example, and as described herein, textual data 110 may berepresentative of additional message 192 provided by user 101 as aninput to chatbot interface 182 of FIG. 1A, e.g., “What's the balance inmy investment account?”. Based on the application of the exemplary NLPalgorithms or techniques described herein to textual data 110, NLPengine 132 may identify discrete linguistic elements (e.g., discretewords, etc.) that include, but are not limited to, “what,” “is,” “the,”“balance,” “in,” “my,” “investment,” and “account,” each of which may bepackaged into a corresponding portion of linguistic element data 112A.Based on the application of any of the NLP algorithms or techniquesdescribed herein to the discrete linguistic elements, e.g., alone or inordered combinations, NLP engine 132 may determine that additionalmessage 192 corresponds to balance inquiry (e.g., the particularservice) involving an investment account held by user 101 (e.g., theparticular product), and further, may identify a particular data type orclass that facilitates a response to the balance inquiry (e.g., accountor transaction data characterizing the investment account of user 101).In some instances, and as described herein, the responsive data type ordata class, which resolves the balance inquiry, may be unavailable tofirst computing system 122 and as such, to executed first chatbot engine124.

NLP engine 132 may package information identifying the particularservice, the particular product, and in some instances, the responsivedata type or data class into corresponding portions of contextualinformation 112B, and NLP engine 132 may provide output data 112, whichincludes linguistic element data 112A and contextual information 112B,as an input to a predictive engine 134. In some instances, predictiveengine 134 may be configured to accept input data having a particularstructure or format (e.g., “encoded” input data), and NLP engine 132 maybe configured to generate structured portions of linguistic element data112A and contextual information 112B that are consistent with, andcompatible with, the particular structure or format of the encoded inputdata. As described herein, and upon execution by first computing system122 (e.g., based on a programmatic command generated by executed firstchatbot engine 124), predictive engine 134 may perform operations thatdetermine capability of executed first chatbot engine 124 to generate aresponse to additional message 192 based on locally accessible data,e.g., as maintained within data repository 126.

In one instance, predictive engine 134 may determine the capability ofexecuted first chatbot engine 124 to generate the response to additionalmessage 192 based on one or more structured or unstructured data recordsmaintained within a locally accessible capability database 126B, e.g.,as maintained within data repository 126. For example, each of the datarecords of capability database 1266 may be associated with acorresponding computing system that operates within environment 100 andthat executes a corresponding chatbot engine, such as, but not limitedto, first computing system 122 that executes first chatbot engine 124.As described herein, each of the computing systems may be associatedwith a financial institution, or a particular business unit of afinancial institution, that provides financial products or services tocustomers, and the data record associated with each of the computingsystems may include identification data that uniquely identifies thecomputing system (such as, but not limited to, an IP address, a networkaddress of a corresponding API of the chatbot engine, etc.) and theassociated financial institution or business unit (such as, but notlimited to, a trade name, a bank identification code (e.g., a SWIFT™code), or a name of the business unit (e.g., retail banking, investmentbanking, insurance, etc.). Further, in some instances, the data recordassociated with each of the computing systems may also link theidentification data to capability data that specifies one or moreproducts or services provided by the corresponding financial institutionor business unit, and further, one or more data classes or typesavailable to or locally maintained by the computing system.

By way of example, first computing system 122 may be associated with aretail banking unit of a particular financial institution, and a datarecord 135A maintained within capability database 126B may includeidentification data that specifies, among other things, an IP address offirst computing system 122, a network address of associated with API128, and a unique identifier of the retail banking unit and particularfinancial institution. Further, data record 135A may also includeinformation that identifies one or more products or services provided tocustomers by the retail banking unit (e.g., balance inquiries regardingdeposit accounts, debit card accounts, credit card accounts, or lines ofcredit, etc.) and additionally, or alternatively, information thatidentifies one or more types or classes of data available for respondingto inquiries identified by executed first chatbot engine 124 (e.g.,account data characterizing balances of deposit accounts, debit cardaccounts, credit card accounts, or lines of credit, etc.). In otherexamples, capability database 126B may also include additional datarecords, such as data record 135B, associated with other business unitsof that particular financial institution, such as an investment bankingunit, and additional data records, such as data record 135C, associatedwith a business unit of a third-party financial institution unrelated tothe particular financial institution, such as a retail banking unit ofthe third-party financial institution. Each of additional data records135B and 135C may be structured in a matter similar to data record 135A,and may include corresponding elements of identification and capabilitydata similar to those described herein in reference to data record 135A.

Referring back to FIG. 1B, predictive engine 134 may access capabilitydatabase 126B, and identify the data records associated with firstcomputing system 122, e.g., data record 135A. Predictive engine 134 mayfurther parse the information maintained within data record 135A todetermine whether executed first chatbot engine 124 is capable ofgenerating messaging data responsive to the inquiry associated withadditional message 192, e.g., balance inquiry regarding user 101'sinvestment account at the particular financial institution, as specifiedwithin contextual information 112B. Although not illustrated in FIG. 1B,if predictive engine 134 were to determine that the retail banking unitof the financial institution provides investment and brokerage servicesto customers, predictive engine 134 may establish the capability ofexecuted first chatbot engine 124 to respond to the inquiry specifiedwithin additional message 192, e.g., based on locally accessiblecustomer, account, or transaction data. In some instances, and based onthe established capability, executed first chatbot engine 124 mayperform operations that generate a response to the unrelated inquirybased on a selected portion of the customer, account, or transactiondata, and transmit the response across communications network 120 toexecuted chatbot application 104, which may present the response withinchatbot interface 182 during the established chatbot session.

Alternatively, based on identified data record 135A, predictive engine134 may determine that the retail banking unit is unable to determinethe account balance of the investment account of user 101 based onlocally accessible customer, account data, or transaction data, e.g.,that data record 135A fails to include information that identifies aprovisioning of balance inquiries regarding investment accounts as aprovided product or service, or that data record 135A does not indicatethat investment account information is locally accessible at firstcomputing system 122. In some instances, predictive engine 134 mayperform operations that parse one or more additional data records ofcapability database 126B, e.g., data record 135B, to identify anadditional computing system, and corresponding chatbot application,capable of responding to the balance inquiry regarding the investmentaccount of user 101 at the particular financial institution.

For example, and based on additional data record 135B, predictive engine134 may determine that second computing system 152, which is associatedwith an investment banking unit of the particular financial institution,locally maintains account data characterizing the balance of theinvestment account of user 101 and as such, that a chatbot engineexecuted by second computing system 152 (e.g., second chatbot engine154) is capable of responding to the unrelated inquiry. In someinstances, illustrated in FIG. 1B, predictive engine 134 may parse datarecord 135B and extract a unique system identifier 114 of secondcomputing system 152, such as an IP address or a MAC address, andprovide extracted system identifier 114 (and linguistic element data112A and contextual information 112B), as inputs to an augmentationmodule 136 of first chatbot engine 124. When executed (e.g., via aprogrammatic command generated by first chatbot engine 124),augmentation module 136 may perform any of the exemplary processesdescribed herein to augment the established chatbot session to includesecond chatbot engine 154 that, when executed by second computing system152, generates messaging data responsive to the unrelated inquiry.

When executed by first computing system 122, augmentation module 136 mayreceive system identifier 114, along with linguistic element data 112Aand contextual information 1126, and may perform operations to establishan existence of a relationship, or a lack thereof, between firstcomputing system 122 and identified second computing system 152. Forexample, a relationship can exist between first computing system 122 andsecond computing system 152 when, among other things, first computingsystem 122 and second computing system 152 are associated with oroperated by a common financial institution, e.g., retail and investmentbanking divisions of the same financial institution. In other instances,a relationship can exist between first computing system 122 and secondcomputing system 152 when the entities that operate these computingsystems are associated with or operated by related entities or financialinstitutions (e.g., that exhibit a corporate relationship, such as aparent and subsidiary, or by entities or financial institutionscharacterized by another formal or informal relationship (e.g., membersof a consortium or industry organization, etc.).

As described herein, the established existence of the relationship mayenable executed second chatbot engine 154 to establish a secure,programmatic communications session with executed chatbot application104 based on a prior authentication between executed chatbot application104 and executed first chatbot engine 124. The prior authenticationbetween executed chatbot application 104 and executed first chatbotengine 124 may also confirm that user 101 consented to an access ofsensitive customer, account, or transaction data by executed firstchatbot engine 124, and in some instances, the existence of thatrelationship may imply that second chatbot engine 154 may rely on theprovided consent to access similarly sensitive customer, account, ortransaction data maintained locally at second computing system 152.

In some instances, and to establish the existence of the relationshipbetween first computing system 122 and second computing system 152,augmentation module 136 may access data records of a relationshipdatabase 126C, e.g., as maintained within data repository 126 of firstcomputing system 122. The data records of relationship database 126C mayassociate a unique system identifier of first computing system 122(e.g., an IP address, a MAC address, etc.) with the unique systemidentifiers of one or more additional computing systems that exhibit anyof the exemplary relationships described herein, such as, but notlimited to, second computing system 152, and further, with additionaldata characterizing the existence or scope of the relationship betweenfirst computing system 122 and each of the one or more additionalcomputing systems.

As illustrated in FIG. 1B, augmentation module 136 may parserelationship database 126C to identify a corresponding one of the datarecords that references system identifier 114 of second computing system152, e.g., data record 138. Augmentation module 136 may process datarecord 138 to access and extract relationship data 140, whichcharacterizes the existence and/or scope of the relationship betweenfirst computing system 122 and second computing system 152. Further, andbased on all or a portion of relationship data 140, augmentation module136 may establish the existence, or lack thereof, of the relationshipbetween first computing system 122 and second computing system 152.

By way of example, and as described herein, extracted relationship data140 may specify that first computing system 122 and second computingsystem 152 represent discrete retail and investment banking units of acommon financial institution. In some instances, and based on extractedrelationship data 140, executed augmentation module 136 may establishthe existence of the relationship between first computing system 122 andsecond computing system 152, and may determine the capability of secondcomputing system 152 to rely on a prior authentication of user 101 withfirst computing system 122, e.g., based on data programmaticallyexchanged between executed chatbot application 104 and executed firstchatbot engine 124 upon establishment of the chatbot session.

Responsive to the established relationship, augmentation module 136 mayperform operations that generate a digital token representative of thatprior authentication and/or consent of user 101 based on the dataprogrammatically exchanged between executed chatbot application 104 andexecuted first chatbot engine 124, such as, but not limited to,authentication token 142 of FIG. 1B. For example, augmentation module136 may generate authentication token 142 based on an application of oneor more token-generation algorithms or processes to input data thatincludes, but is not limited to, an identifier of client device 102and/or executed chatbot application 104 (e.g., a network address ofclient device 102, such as an IP address, an identifier or address of aprogrammatic interface of executed chatbot application 104, etc.), aportion of messaging data 108 (e.g., that characterizes the inquiryspecified within additional message 192), and additionally, oralternatively, portions of linguistic element data 112A and contextualinformation 112B. In some instances, authentication token 142 may becharacterized by a structure specified by, or recognized by, secondchatbot engine 154 and/or a programmatic interface of second chatbotengine 154 (e.g., application programming interface (API) 156 of FIG.1B), and examples of authentication token 142 include, but are notlimited to, a cryptogram, a hash, random number, or other element ofcryptographic data having a predetermined length or structure (e.g., anumber of leading or trailing zeroes, etc.).

As described herein, authentication token 142 may confirm, to API 156,that executed first chatbot engine 124 is authorized to access secondchatbot engine 154 and further, to access sensitive customer, account,or transaction data locally maintained by second computing system 152(e.g., within one or more tangible, non-transitory memories), whichwould ordinarily be accessible to executed second chatbot engine 154,but that would ordinarily be inaccessible to executed first chatbotengine 124. Further, and in some instances, one or more of the exemplaryprocesses described herein that generate authentication token 142 inresponse to the established relationship between first computing system122 and second computing system 152 can be implemented in addition to,or as an alternate to, conventional token-based authenticationprocesses, such an OAuth protocol, that would otherwise be implementedbetween first chatbot engine 124 (e.g., as executed by first computingsystem 122) and API 156 (e.g., as associated with second chatbot system152) to confirm an authentication of user 101 and a level of consentprovided by user 101.

In some instances, not illustrated in FIG. 1B, augmentation module 136perform operations that store authentication token 142 in acorresponding portion of relationship database 126C, e.g., within datarecord 138, and may associate authentication token 142 with storedsystem identifier 114 and relationship data 140, which identify andcharacterize second computing system 152. For example, authenticationtoken 142 may be associated with a fixed period of validity (e.g., oneminute, five minutes, thirty minutes, etc.) or a variable period ofvalidity (e.g., based on activity within the established chatbotsession, etc.), and in certain exemplary embodiments, augmentationmodule 136 may perform operations that extract a locally stored copy ofauthentication token 142 from data record 138 in response to theestablished relationship between first computing system 122 and secondcomputing system 152 (e.g., during the fixed or variable period ofvalidity and in addition to, or as an alternate to, the exemplaryprocessed described herein that generate authentication token 142).

Referring back to FIG. 1B, augmentation module 136 may also performoperations that generate elements of data, e.g., baseline session data144, that identify and characterize the established chatbot sessionbetween chatbot application 104 (e.g., as executed by client device 102)and first chatbot engine 124 (e.g., as executed by first computingsystem 122). In some instances, the generated elements of baselinesession data 144 may include, but are not limited to: session identifier176; information that uniquely identifies client device 102 and/orexecuted chatbot application 104 (e.g., a network address of clientdevice 102, such as an IP address, an identifier or address of aprogrammatic interface of executed chatbot application 104, etc.); rawmessage data characterizing the inquiry specified within additionalmessage 192 (e.g., a portion of textual data 110); and additionally, oralternatively, portions of linguistic element data 112A and contextualinformation 112B, which specify the discrete linguistic elementsincluded within additional message 192 and the established context ormeaning of the combination of the discrete linguistic elements. In someinstances, augmentation module 136 may perform operations that packageauthentication token 142 and baseline session data 144 intocorresponding portions of an augmentation request 146, which firstcomputing system 122 may transmit, across communications network 120 tosecond computing system 152, e.g., using any appropriate communicationsprotocol.

A programmatic interface maintained by second computing system 152,e.g., API 156, may receive augmentation request 146. In some instances,API 156 may perform operations that access authentication token 142within augmentation request 146, and that determine whether a structureof authentication token 142 (e.g., a token length, a number of leadingor trailing zeroes, etc.) is consistent with, or corresponds to, apredetermined token structure associated with API 156 (e.g., an“expected” token structure). In some instances, second computing system152 may store data characterizing the expected token structure, or datacharacterizing one or more token-generation algorithms or processes thatgenerate an authentication token having the expected token structurebased on corresponding input data, within one or more tangible,non-transitory memories, e.g., within a portion of data repository 158accessible to API 156 and second chatbot engine 154.

If, for example, API 156 were to detect an inconsistency between theexpected token structure and the structure of authentication token 142,API 156 may decline to establish communications with executed firstchatbot engine 124, and second computing system 152 may generate andtransmit an error message indicative of the detected inconsistency tofirst computing system 122, e.g., across communications network 120using any appropriate communications protocol. Alternatively, if API 156were to detect a consistency between the expected token structure andthe structure of authentication token 142, API 156 may establish asecure, programmatic channel of communications between first chatbotengine 124 (e.g., as executed by first computing system 122) and secondchatbot engine 154 (e.g., as executed by second computing system 152).

In some instances, API 156 may route augmentation request 146, whichincludes authentication token 142 and baseline session data 144, to asecurity module 160 of second chatbot engine 154, which may performoperations that validate authentication token 142. By way of example,when executed by second computing system 152 (e.g., based one or morecommands programmatically generated by executed second chatbot engine154), security module 160 may perform operations that compute a localcopy of authentication token 142 using any of the exemplary processesdescribed herein, and that validate authentication token 142, and theprior authentication of the identity of user 101 during the establishedchatbot session, based on a comparison of the received and locallycomputed copies of authentication token 142.

If security module 160 were to detect an inconsistency between thereceived and locally computed copies of authentication token 142,security module 160 may decline to validate authentication token 142,and second chatbot engine 154 may decline to rely on the priorauthentication when establishing a secure, programmatic communicationssession with executed chatbot application 104 (e.g., which augments theestablished chatbot session to include second chatbot engine 154). Inone instance, and in response to the failed validation, executed secondchatbot engine 154 may generate and transmit an error message indicativeof the failed validation across network 120 to first computing system122, which may cause executed first chatbot engine 124 to performoperations that select another computing system capable of responding tothe inquiry specified within additional message 192, that prompt user101 to obtain information responsive to the inquiry throughout-of-session channels, or that initiate one or more token-basedauthentication token-based authentication and consent processes, such asan OAuth protocol, in conjunction with application programs executed byclient device 102 and second computing system 152.

If, however, security module 160 were to establish a consistency betweenthe received and locally computed copies of authentication token 142,security module 160 may validate authentication token 142, and secondchatbot engine 154 may rely on that prior authentication whenestablishing the secure, programmatic communications session withchatbot application 104 (e.g., as executed by client device 102). Inresponse to the successful validation of the prior authentication bysecurity module 160, executed second chatbot engine 154 may performoperations that establish a handshake between API 156 and thecommunications interface of client device 102, which facilitates secure,programmatic communications and exchanges of data between second chatbotengine 154 (e.g., as executed by second computing system 152) andchatbot application 104 (e.g., as executed by client device 102).

Security module 160 may also perform operations that store augmentationrequest 146 including now-validated authentication token 142 andbaseline session data 144, within one or more tangible, non-transitorymemories, e.g., within one or more data records of session database 162of data repository 158. Further, and responsive to the validation of theprior authentication, security module 160 may route baseline sessiondata 144 to one or more additional application modules of second chatbotengine 154 that, upon execution, perform any of the exemplary processesdescribed herein to generate a response to the unrelated inquiry (e.g.,the request for the balance of user 101's investment account) based onportions of baseline session data 144 and locally accessible elements ofsensitive customer, account, or transaction data, and to provision thegenerated response for presentation within chatbot interface 182 duringan augmented chatbot session that includes chatbot application 104,first chatbot engine 124, and second chatbot engine 154

By way of example, as illustrated in FIG. 1B, security module 160 mayroute baseline session data 144 to a response generation module 164 ofexecuted second chatbot engine 154. As described herein, baselinesession data 144 may include a unique identifier of user 101 (e.g., alogin credential, etc.), client device 102 (e.g., a network address,such as an IP address, etc.), or chatbot application 104 (e.g., anaddress of a corresponding programmatic interface, etc.). Further, asalso described herein, baseline session data 144 may also associate eachof the unique identifiers with additional information that characterizesthe inquiry specified within additional message 192, such as, but notlimited to, all or a portion of textual data 110, linguistic elementsdata 112A and additionally, or alternatively, contextual information112B.

When executed by second chatbot engine 154 (e.g., based on one or moreprogrammatically generated commands), response generation module 164 mayparse baseline session data 144 to extract one or more of the uniqueidentifiers of user 101, client device 102, and/or executed chatbotapplication 104, and all or a portion of contextual information 112B,which characterizes the unrelated inquiry, the product or servicespecified by the unrelated inquiry, and further, the responsive datatype or class. In some instances, response generation module 164 mayaccess one or more elements of the sensitive customer, account, ortransaction data (e.g., as maintained within data repository 158) thatare consistent with the extracted identifiers and the extracted portionof contextual information 112B, and based on the accessed elements ofsensitive customer, account, or transaction data, generate a response tothe unrelated inquiry and package the generated response into one ormore elements of response data 166.

Further, in some examples, response generation module 164 may alsopackage, into the one or more elements of response data 166, certainportions of baseline session data 144 that identify and characterize theinquiry, such as, but not limited to, an inquiry type (e.g., the balanceinquiry specified within contextual information 112B) and a subject ofthe inquiry (e.g., the investment account of user 101, as specifiedwithin contextual information 112B). In some instances, the packagedportions of baseline session data 144 may correspond to metadata that,when associated with the generated response within the one or moreelements of response data 166, further describes or characterizes thegenerated response and facilitates a generation of messaging data thatresponds to additional message 192 within an augmented chatbot session.

By way of example, and as described herein, the unrelated inquiry maycorrespond to a balance inquiry associated with an investment accountmaintained on behalf of user 101 by the investment banking unit of thefinancial institution (e.g., “What's the current balance in myinvestment account?”). Using any of the exemplary processes describedherein, response generation module 164 may access locally storedbaseline session data 144, e.g., as maintained within the data recordsof session database 162, and extract a unique identifier of user 101(e.g., the login credential of user 101, etc.) and/or a uniqueidentifier of client device 102 (e.g., the IP address of client device102). Response generation module 164 may also extract, from accessedbaseline session data 144, a portion of contextual information 112B thatcharacterizes the inquiry as a balance inquiry and that identifieslocally maintained investment account data as a responsive data type orclass.

In some instances, illustrated in FIG. 1B, response generation module164 may access investment account database 163, e.g., as maintainedwithin data repository 158, and identify one or more data records 163Athat include or reference the unique identifier of user 101 or clientdevice 102. For example, accessed data records 163A may include acurrent balance of the investment account held by user 101 (e.g.,$121,375.00), and response generation module 164 may perform operationsthat extract the current balance from accessed data records 163. Inother instances, accessed data records 163A may identify one or moresequentially ordered transactions involving user 101's investmentaccount during one or more temporal intervals, and response generationmodule 164 may compute the current balance of the investment accountbased on the one or more sequentially ordered transactions withinaccessed data records 163A. Response generation module 164 may alsoperform operations that package the extracted or computed currentbalance of the investment account within the one or more elements ofresponse data 166, along with additional portions of contextualinformation 112B that identify and characterize the balance inquiry anduser 101's investment account.

In some exemplary embodiments, as described herein, response generationmodule 164 may generate all, or a portion, of response data 166 based acontextual analysis of additional message 192 performed by executedfirst chatbot engine 124, e.g., based on contextual information 112Bderived from an application of one or more of the natural languageprocessing techniques to textual data 110 by NLP engine 132 of executedfirst chatbot engine 124. In other exemplary embodiments, second chatbotengine 154 may also include one or more local natural languageprocessing (NLP) modules (not illustrated in FIG. 1B), which, inresponse to a successful validation of authentication token 142 bysecurity module 160, apply one or more of the natural languageprocessing techniques described herein to all or a portion of textualdata 110 included within baseline session data 144. Based on theapplication of these natural language processing algorithms, the one ormore local NLP modules of second chatbot engine 154 may perform any ofthe exemplary processes described herein to identify the one or morediscrete linguistic elements within textual data 110, and to generatelocal contextual information that establishes the context and meaning ofcombinations of the discrete linguistic elements within textual data110, each of which may be provided as an input to response generationmodule 164.

Referring back to FIG. 1B, response generation module 164 may provideresponse data 166 as an input to a messaging module 168 of secondchatbot engine 154. In some instances, when executed by second computingsystem 152, messaging module 168 may perform any of the exemplaryprocesses described herein to generate a response message 170 thatincludes one or more selected portions of response data 166, isresponsive to the unrelated inquiry, and when presented within chatbotinterface 182, facilitates an ongoing and simulated conversation betweenuser 101 and the chatbots generated or hosted programmatically by firstchatbot engine 124 and by second chatbot engine 154 during an augmentedchatbot session.

For example, response data 166 may indicate that the investment accountheld by user 101 is characterized by a current balance of $121,375.00,and may include additional information, e.g., metadata, that identifiesthe specified inquiry as a balance inquiry (e.g., the inquiry type)associated with the investment account held by user 101 (e.g., thesubject of the inquiry). Messaging module 168 may parse response data166 to extract the current balance $121,375.00 and the elements ofmetadata (e.g., that identify the inquiry type and the subject of theinquiry), and generate elements of textual content 174 that areconsistent with extracted elements of metadata and identify theextracted current balance of the investment account. In some instances,when rendered for presentation within chatbot interface 182, thegenerated elements of textual content may correspond to, and represent,a response to the introductory message 190 within the ongoing andfacilitated conversation between the between user 101 and the chatbotsgenerated or hosted programmatically by first chatbot engine 124 andsecond chatbot engine 154, e.g., during the augmented chatbot sessiondescribed herein.

In some instances, messaging module 168 may generate the elements oftextual content 174 in accordance in accordance with one or moreresponse templates and additionally, or alternatively, in accordancewith one or more predetermined rules that specify appropriate responsemessages. For example, each of response templates or predetermined rulesmay be associated with a particular inquiry type (e.g., a balanceinquiry, a credit inquiry, etc.) or a particular inquiry subject (e.g.,an investment account, a credit card account, etc.), and secondcomputing system 152 may maintain data identifying and specifying eachof the response templates or predetermined rules within a correspondingportion of data repository 158, e.g., within template and rules datastore 172.

For example, an upon receipt of response data 166, messaging module 168may access template and rules data store 172 and extract template data173 that specifies a response template consistent with the correspondinginquiry type (e.g., the balance inquiry) or the corresponding inquirysubject (e.g., the investment account of user 101). As described herein,the corresponding inquiry type and/or inquiry subject may be defined asmetadata within response data 166, or in other examples, messagingmodule 168 may perform operations that extract the corresponding inquirytype and/or inquiry subject from portions of baseline session data 144(e.g., from contextual information 112B) maintained within the datarecords of session database 162. The response template specified withintemplate data 173 may, in some instances, include predetermined textualcontent associated with the response to the balance inquiry (or theinvestment account) along with placeholder textual content capable ofpopulation or replacement by messaging module 168 with correspondingportions of response data 166.

By way of example, the response template within template data 173 (e.g.,corresponding to the balance inquiry) may specify predetermined andplaceholder textual content that includes, but is not limited to “Thecurrent balance of your [[Inquiry Subject]] is [[Current Balance]].” Insome instances, messaging module 168 may parse the response template toidentify the elements of placeholder textual content, e.g., “[[InquirySubject]]” and “[[Current Balance]],” and may perform operations thatgenerates populated template data by populating, or replacing, each ofthe elements of placeholder textual content within a correspondingportion of response data 166 or locally stored baseline session data144.

For example, messaging module 168 may generate populated template datathat replaces “[[Inquiry Subject]]” with textual content representativeof the inquiry subject (e.g., “investment account” extracted from eitherresponse data 166 or from baseline session data 144), and that replaces“[[Current Balance]]” with textual content representative of the$121,375.00 current balance of the investment account (e.g., asextracted from response data 166), and the populated template data mayspecify textual content that includes, but is not limited to, “Thecurrent balance of your investment account is $121,375.00.” In someinstances, messaging module 168 may package the generated template datainto a corresponding portion of response message 170, e.g., as textualcontent 174.

The disclosed exemplary embodiments are, however, not limited toprocesses that generate textual content 174 in accordance with one ormore response templates or one or more predetermined rules. In otherexamples, not illustrated in FIG. 1B, messaging module 168 may generatedynamically all or a portion of textual content 174 based on anapplication of one or more machine learning processes, one or moreartificial intelligence processes (e.g., a neural network modelimplemented through a distributed or cloud-based computing system), orone or more stochastic statistical models (e.g., a Markov process) tostructured input that includes, but is not limited to, portions oftextual data 110 (e.g., that defines the balance inquiry associated withuser 101's investment account, as specified within additional message192) and to portions of response data 166 (e.g., that specifies the$121,375.00 current balance of the investment account, etc.).

In some instances, the one or more machine learning processes,artificial intelligence processes, or stochastic statistical models maycollectively establish a dynamic natural language generation (NLG)process that, when applied to any of the structured inputs describedherein, dynamically generates textual content that responds to theinquiry specified within additional message 192 and further, mimicshuman-generated content during an ongoing conversation with user 101.The dynamic NLG model may be adaptively trained against, and improvedusing, selected elements of training data that include, but are notlimited to, data characterizing prior chatbot or messaging sessionsinvolving user 101, other users of second chatbot engine 154, anddigitized or electronic copies of correspondence prepared by, orreceived by, user 101 or the other users. Further, and by way ofexample, the dynamic NLG model may be deemed trained when a quality oran accuracy of generated textual content satisfies a predeterminedmetric (e.g., the accuracy of the generated textual context exceeds athreshold accuracy, etc.), and messaging module 168 may generatedynamically all or a portion of textual content 174 based theapplication of the now-trained dynamic NLG model to one or more elementsof the structured input data, as described herein.

As illustrated in FIG. 1B, messaging module 168 may perform operationsthat package the generated textual content 174 into a correspondingportion of response message 170. Messaging module 168 may also performoperations that package, into additional portions of response message170, information that includes, but is not limited to: (i) sessionidentifier 176 of the established chatbot session between chatbotapplication 104 (e.g., as executed by client device 102) and firstchatbot engine 124 (e.g., as executed by first computing system 122),which messaging module 168 may extract from a corresponding portion oflocally maintained baseline session data 144 (e.g., within the datarecords of session database 162); and (ii) data 178 that identifies orcharacterizes second chatbot engine 154 or second computing system 152,e.g., the investment banking unit of the financial institution thatoperates first computing system 122 and second computing system 152. Insome instances, when processes by executed chatbot application 104,session identifier 176 and/or data 178 causes executed chatbotapplication 104 to establish a secure, programmatic channel ofcommunications with executed second chatbot engine 154, and toinstantiate an augmented chatbot session that facilitates an ongoing andsimulated conversation between user 101, executed first chatbot engine124, and executed chatbot engine 154. Messaging module 168 may performoperations that cause second computing system 152 to transmit responsemessage 170 across communications network 120 to client device 102.

Referring to FIG. 1C, a programmatic interface associated with chatbotapplication 104, such as API 105, may receive and route response message170 to a session augmentation module 116 of chatbot application 104.Upon execution by chatbot application 104, session augmentation module116 may receive response message 170 and may perform operations thatparse response message 170 to extract session identifier 176, whichcorresponds to the unique alphanumeric session identifier received byexecuted second chatbot engine 154 from executed first chatbot engine124, e.g., as a portion of baseline session data 144. Further, sessionaugmentation module 116 may also access local session data 115 andobtain a local session identifier 117, which corresponds to a copy ofsession identifier 176 received by executed chatbot application 104 fromexecuted first chatbot engine 124 upon initiation of the existingchatbot session.

Session augmentation module 116 may perform operations that determinewhether local session identifier 117 is consistent with, and correspondsto, extracted session identifier 176. If, for example, sessionaugmentation module 116 were to detect an inconsistency between thelocal and extracted session identifiers, session augmentation module 116may decline to establish a secure, programmatic channel ofcommunications between executed chatbot application 104 and executedsecond chatbot engine 154, and may further decline to augment theexisting chatbot session to include executed second chatbot engine 154.In some instances, however, executed chatbot application 104 maymaintain the establish communications channel, and the establishedchatbot session, with executed first chatbot engine 124, and user 101may continue to interact with executed first chatbot engine 124 duringthe established chatbot session via chatbot interface 182.

Alternatively, if session augmentation module 116 were to detect aconsistency between local session identifier 117 and extracted sessionidentifier 176, session augmentation module 116 may perform operationsthat establish the secure, programmatic channel of communicationsbetween executed chatbot application 104 and executed second chatbotengine 154, e.g., via API 105 of chatbot application 104 and API 156 ofsecond chatbot engine 154. Further, and responsive to the establishedconsistency, session augmentation module 116 may perform furtheroperations that instantiate the augmented chatbot session thatfacilitates the ongoing and simulated conversation between user 101,executed first chatbot engine 124, and executed chatbot engine 154, andthat renders and presents the response to the inquiry specified withinadditional message 192 on a portion of chatbot interface 182.

For example, and responsive to the responsive to the establishedconsistency, session augmentation module 116 may provide responsemessage 170 as an input to a chatbot interface module 118 of chatbotapplication 104. Upon execution by chatbot application 104, chatbotinterface module 118 may perform operations that parse response message170 to extract textual content 174, and that generate one or moreinterface elements 119 representative of textual content 174. In someinstances, chatbot interface module 118 may provide interface elements119 to a display unit of client device 102 (not illustrated in FIG. 1C),which may present interface elements 119 within a corresponding portionof chatbot interface 182 during the now-augmented chatbot session, e.g.,as response 194 indicating that “The current balance of your investmentaccount is $121,375.00.”

As illustrated in FIG. 1C, executed chatbot application 104 may performoperations that cause the display unit to present introductory message190 (e.g., as generated by executed first chatbot engine 124) andresponse 194 (e.g., as generated by executed second chatbot engine 154)on chatbot interface 182 without any identifiers of first chatbot engine124 or second chatbot engine 154. As such, in some instances, user 101may be unaware of the dynamic and programmatic augmentation of theestablished chatbot session to include both first chatbot engine 124 andsecond chatbot engine 154 based on a performance of the collectiveoperations by chatbot application 104, first chatbot engine 124, andsecond chatbot engine 154.

In other instances, not illustrated in FIG. 1C, chatbot interface module118 may perform operations that generate, and render for presentationwithin chatbot interface 182, additional interface elements thatidentify a corresponding chatbot that generated one or more ofintroductory message 190 or response 194, e.g., a corresponding one ofexecuted first chatbot engine 124 or executed second chatbot engine 154.For example, these additional interface elements may include a firsttextual or graphical indicator of the retail banking unit of thefinancial institution, which may be presented within chatbot interface182 in conjunction with, or in proximity to, introductory message 190(e.g., as generated by first chatbot engine 124 associated with theretail banking unit), and a second textual of graphical indicator of theinvestment banking unit of the financial institution, which may bepresented within chatbot interface 182 in conjunction with, or inproximity to, response 194 (e.g., as generated by second chatbot engine154 associated with the investment banking unit). The disclosedembodiments are, however, not limited to these exemplary textualindicators and graphical indicators, and chatbot interface module 118may perform operations that present any additional or alternateindicator in conjunction with a corresponding message that would beappropriate to identify an underlying chatbot, business unit, orfinancial institution, including audible or tactile indicators generatedby client device 102.

In some exemplary embodiments, as described in reference to FIGS. 1A-1C,first computing system 122 and second computing system 152 may performoperations that augment an existing chatbot session between user 101(e.g., via chatbot interface 182 generated by executed chatbotapplication 104) and a first chatbot programmatically generated andhosted by first computing system 122 (e.g., based on operationsperformed by executed first chatbot engine 124) to include a secondchatbot programmatically generated and hosted by second computing system152 (e.g., based on operations performed by executed second chatbotengine 154). As described herein, and based on a detected relationshipbetween first computing system 122 and second computing system 152,executed second chatbot engine 154 may rely on a prior authentication ofuser 101 by executed first chatbot engine 124 to establish a secure,programmatic channel of communications with executed first chatbotengine 124, and to receive, across the established communicationschannel, session data characterizing an unrelated inquiry posed by user101 during the existing chatbot session, e.g., to which executed firstchatbot engine 124 may be incapable of responding.

Further, and based on the prior authentication of user 101 by executedfirst chatbot engine 124, executed second chatbot engine 154 may performany of the exemplary processes described herein to generate a responseto the inquiry based on locally accessible elements of sensitivecustomer, account, or transaction data associated with user 101, and toprovision that response to executed chatbot application 104 forpresentation within chatbot interface 182 during the augmented chatbotsession, e.g., automatically and without further intervention from user101. In some instances, certain of these exemplary embodiments may beimplemented in addition to, or as an alternate to, token-basedauthentication and consent protocols, such as an OAuth protocol, whichrequire an additional authentication of user 101, and a provision ofconsent by user 101, prior to establishing secure, programmaticcommunications between executed first chatbot engine 124 and executedsecond chatbot engine 154 (and between executed second chatbot engine154 and executed chatbot application 104).

In other instances, the inquiry posed by user 101 may referenceinformation available to one or more third-party computing systemsunrelated to first computing system 122, second computing system 152,and further, to executed chatbot application 104 provisioned to clientdevice 102 by first computing system 122. By way of example, and duringthe existing chatbot session between executed chatbot application 104and executed first chatbot engine 124 (e.g., associated with the retailbanking unit of the financial institution), user 101 may provide inputto client device 102 that specifies a further inquiry associated with aproduct or service offered to user 101 by a third-party financialinstitution unrelated to the retail banking unit or investment bankingunit of the financial institution (e.g., a third-party product orservice). In some instances, first chatbot engine 124 may be incapableof generating a message responsive to the further inquiry, as firstcomputing system 122 does not maintain data characterizing thethird-party product or service. Further, executed first chatbot engine124 may also be incapable of establishing a secure channel ofcommunication with an executed chatbot engine associated with thethird-party financial institution based on the prior authentication ofuser 101, as no relationship exists between first computing system 122and a computing system associated with the third-party financialinstitution, e.g., a third-party computing system, which maintainselements of sensitive customer, account, or transaction data responsiveto the further inquiry.

In some exemplary embodiments, described below in reference to FIGS.2A-2C, executed chatbot engine 124 may perform operations that establisha secure channel of communications with the third-party computing systemand initiate an augmentation of the established chatbot session toinclude a third-party chatbot engine executed by that third-partycomputing system based not on the prior authentication of user 101during the established chatbot session, but instead based on dataindicative of a successful outcome of one or more of the token-basedauthentication and consent processes described herein. Examples of thesetoken-based authentication and consent processes include, but are notlimited to, an OAuth protocol collectively implemented by applicationprograms executed by client device 102, first computing system 122, andthe third-party computing system, e.g., in response to an access requestgenerated by executed first chatbot engine 124 and transmitted to aprogrammatic interface of the third-party chatbot engine.

In some instances, the access request may include, but is not limitedto, data that uniquely identifies user 101 (e.g., a login credential, adigital identifier within an open-banking environment, etc.) or clientdevice 102 (e.g., an IP address or a MAC address), along with additionaldata that uniquely identifies first computing system 122 or executedfirst chatbot engine 124 (e.g., an IP address of first computing system122, an application cryptogram generated by or assigned to executedfirst chatbot engine 124, etc.). The access request may also include,but is not limited to, additional data that characterizes the requestedaccess, such as session identifier 176 of the established chatbotsession or information specifying the requested augmentation of theestablished chatbot session or data response to the further inquiry,e.g., the elements of sensitive customer, account, or transaction datamaintained by the third-party computing system.

The receipt of the access request at the programmatic interface of thethird-party chatbot engine may cause the third-party computing system togenerate, and transmit a notification of the requested access (e.g., theaccess requested by executed first chatbot engine 124) to client device102, e.g., across communications network 120 using any appropriatecommunications protocol. Client device 102 may receive the notification,and may execute one or more application programs (e.g., a third-partymobile application provisioned by the third-party computing system) thatgenerate and present, on the display unit, a digital interface thatprompts user 101 to provide or deny explicit consent to the requestedaccess and to provide one or more appropriate login or authenticationcredentials that confirm the explicitly provided or denied consent. Byway of example, client device 102 may execute the one or moreapplications, such as the third-party mobile application, in thebackground, and upon receipt of the notification, may perform operationsthat promote the executed third-party mobile application to theforeground for presentation of the digital interface. In otherinstances, the receipt of the notification may cause client device 102to generate one or more programmatic commands to execute the one or moreapplication programs, e.g., the third-party mobile application.

For example, the digital interface may include one or more interfaceelements that identify the type of access requested by executed firstchatbot engine 124, such as, but not limited to, the augmentation of theexisting chatbot session to include the third-party chatbot engineand/or the generation of messaging data based on the elements ofsensitive customer, account, or transaction data by that third-partychatbot engine. Further, the digital interface may include one or moreinterface elements that prompt user 101 to provide or deny the requestedconsent (e.g., a selectable icon, a checkbox, etc.) and further, toinput one or more authentication or login credentials that confirm thegranted or denied consent, such as, but not limited to, an alphanumericlogin credential, an alphanumeric password, or a biometric credential(e.g., a thumbprint scan, a facial image, etc.). In some instances, user101 may provide, via the input unit, input to client device 102 thatspecifies the granted or denied consent and the one or moreauthentication credentials, and the executed third-party application mayperform operations that authenticate an identity of user 101 based acomparison between the specified authentication credentials and locallymaintained credential data. Responsive to a successful authentication ofthe identity of user 101, the executed third-party application maygenerate consent data indicative of the successful authentication ofuser 101's identity and the granted or denied consent, and client device102 may transmit the consent data to the third-party computing systemvia the programmatic interface, e.g., across communications network 120using any appropriate communications protocol.

The third-party computing system may receive the consent data, and mayprocess the consent data to determine whether user 101 granted consentfor the access requested by executed first chatbot engine 124. Based onthe granted consent, the identified computing system may performoperations that generate a digital token, cryptogram, or other elementof cryptographic data, e.g., an OAuth token, indicative of successfulauthentication of user 101's identity and the granted consent, and maystore the OAuth token within one or more tangible, non-transitorymemories, e.g., in conjunction with elements of the received requestthat identify first computing system 122 or executed first chatbotengine 124 (and additionally, or alternatively, session identifier 176associated with the established chatbot session). In some instances, theOAuth token may be characterized by a predetermined structure or formatrecognizable by each of the participants in the OAuth process, such asfirst computing system 122, and the third-party computing system mayperform operations that transmit the OAuth token across communicationsnetwork 120 to first computing system 122, e.g., in response to therequest generated by executed first chatbot engine 124.

First computing system 122 may receive the OAuth token, and may storethe received OAuth token within one or more tangible, non-transitorymemories, e.g., within the data records of relationship database 126Cassociated with the identified computing system. As described herein,the OAuth token may confirm an established permission of executed firstchatbot engine 124 to access the programmatic interface associated withthe third-party computing system, may confirm to the third-party chatbotengine that user 101 consented to the augmentation of the existingchatbot session, e.g., between executed first chatbot engine 124 andexecuted chatbot application 104, and to the exchange of sensitivecustomer, account, and transaction data during that augmented chatbotsession. In some instances, described below in reference to FIGS. 2A-2C,executed first chatbot engine 124 and the executed third-party chatbotengine may perform any of the exemplary processes described herein toestablish a secure, programmatic channel of communications based on theprovisioned OAuth token, and to initiate the augmented chatbot sessionsthat include the third-party chatbot engine, which responds to thefurther inquiry based on elements of sensitive customer, account, ortransaction data locally maintained at the third-party computing system(and in accessible to first computing system 122).

Referring to FIG. 2A, and in further response to the presentation ofintroductory message 190 within chatbot interface 182, user 101 mayprovide input 202 to client device 102 (e.g., via the correspondinginput unit using any of the exemplary processes described herein) thatspecifies an additional inquiry unrelated to the products or servicesprovided to user 101 by the retail banking unit of the financialinstitution. By way of example, and as described herein, the additionalinquiry may include a balance inquiry associated with a credit cardaccount issued to user 101 by a third-party financial institutionunrelated to that financial institution (e.g., “What is the balance onmy third-party credit card?”).

Upon execution by client device 102, chatbot application 104 may receiveinput 202, and may perform any of the exemplary processes describedherein to render input 202 for presentation as an additional message 204within chatbot interface 182 on the corresponding display unit. Chatbotapplication 104 also may perform any of the exemplary processesdescribed herein to package all, or a selected portion, of input 202into corresponding portions of messaging data 206. In some instances,messaging data 206 may include textual content that specifies theadditional unrelated inquiry, which chatbot application 104 may extractfrom, derive from, or generate on the basis of input 202, along withsession identifier 176 and temporal data characterizing a time or dateat which user 101 provided input 202 to client device 102.

As described herein, messaging data 206 may also include a uniqueidentifier of user 101 (e.g., a login credential, a digital identifier,etc.), a unique identifier of client device 102 (e.g., an IP address, aMAC address, etc.) and additionally, or alternatively, an applicationcryptogram generated by executed chatbot application 104 (e.., thatuniquely identifies executed chatbot application 104). Executed chatbotapplication 104 may perform additional operations that cause clientdevice 102 to transmit messaging data 206 across communications network120 to first computing system 122, e.g., using any appropriatecommunications protocol.

First computing system 122 may receive messaging data 108 through asecure programmatic interface, such API 128 of executed chatbot engine124, and executed first chatbot engine 124 may perform any of theexemplary processes described herein to verify received messaging data206 is associated with, and was generated during, the establishedchatbot session, with executed chatbot application 104. In one instance,and in response to a failed verification of messaging data 206, executedfirst chatbot engine 124 may perform operations that discard receivedmessaging data 206, terminate the established chatbot session, andgenerate and transmit and error message to client device 102, e.g.,through API 128 (not illustrated in FIG. 2A).

In other instances, and responsive to a successful verification ofmessaging data 206, executed first chatbot engine 124 may performoperations that associate messaging data 206 with the establishedchatbot session and store received messaging data 206 within the one ormore data records of session database 126A associated with theestablished chatbot session (e.g., that include or reference sessionidentifier 176 of the established chatbot session), along with alongwith temporal data characterizing a time or date at which client device102 generated messaging data 206, or at which first computing system 122received messaging data 206. Executed first chatbot engine 124 may alsoperform any of the exemplary processes described herein to identify andcharacterize one or more inquiries specifies within messaging data 206(the balance inquiry related to the third-party credit card accountissued to user 101 by the third-party financial institution), toidentify a computing system that operates within environment 100 and isconfigured to, or capable of, accessing data responsive each of theinquiries and further, to augment the established chatbot session toinclude an additional chatbot engine that, when executed by thatidentified computing system, generates messaging data responsive to eachof the identified inquiries.

Referring to FIG. 2B, messaging data 206 may be routed to sessionmanagement module 130 of executed first chatbot engine 124, which mayperform operations that parse messaging data 108 to extract textual data208 (e.g., representative of additional message 204, including “What isthe balance on my third-party credit card?”). In some instances, sessionmanagement module 130 may generate a programmatic command that executesa natural language processing (NLP) engine 132 of first chatbot engine124, and as illustrated in FIG. 2B, session management module 130 mayprovide extracted textual data 208 as an input to executed NLP engine132. In other instances, consistent with the disclosed exemplaryembodiments, a functionality of session management module 130 may beperformed by executed NLP engine 132, and API 128 may route receivedmessaging data 206 directly to executed NLP engine 132, which mayprocess messaging data 206 and extract textual data 208.

NLP engine 132 may receive textual data 208, and may apply one or moreof the exemplary natural language processing techniques or processes toall, or a selected portion, of textual data 208. Based on theapplication of these natural language processing algorithms processes,NLP engine 132 may perform any of the exemplary processes describedherein to identify one or more discrete linguistic elements (e.g., aword, a combination of morphemes, a single morpheme, etc.) withintextual data 208, and establish a context and a meaning of combinationsof the discrete linguistic elements, e.g., based on the identifieddiscrete linguistic elements, relationships between these discretelinguistic elements, and relative positions of these discrete linguisticelements within textual data 208. In some instances, NLP engine 132 maygenerate output 210 that includes, but is not limited to, linguisticelement data 210A and contextual information 210B.

As described herein, linguistic element data 210A may include each ofthe discrete linguistic elements identified within textual data 208 byNLP engine 132, along with position data characterizing a position orlocation of each of the discrete linguistic elements within textual data208. Further, contextual information 210B may specify the establishedcontext or meaning of the combination of the discrete linguisticelements and in some instances may identify a product or serviceassociated with or requested by additional message 204, e.g., asspecified by the inquiry within textual data 208. Additionally, oralternatively, contextual information 112B may also identify one or moreelements of data or types of data that, if accessible to first chatbotengine 124, would facilitate a local generation of a response to theinquiry.

By way of example, and as described herein, textual data 208 may berepresentative of additional message 204 displayed on chatbot interface182 of FIG. 2A. Based on the application of the exemplary naturallanguage processing algorithms described herein to textual data 110208NLP engine 132 may parse textual data 110 and extract discretelinguistic elements (e.g., discrete words) that include, but are notlimited to, “what,” “is,” “the,” “balance,” “on,” “my,” “third-party,”“credit,” and “card,” each of which may be packaged into a correspondingportion of linguistic element data 210A. Based on any of these exemplarynatural language processing algorithms described herein to the discretelinguistic elements, e.g., alone or in ordered combinations, NLP engine132 may determine that additional message 204 corresponds to a balanceinquiry (e.g., the particular service) involving a credit card accountissued by the third-party financial institution and held by user 101(e.g., the particular product), and further, a particular data type orclass that facilitates a response to the balance inquiry (e.g., accountdata characterizing the third-party credit card account held by user101). As described herein, the particular data type or data class, whichresolves the balance inquiry, may be unavailable first computing system122 and as such, unavailable to executed first chatbot engine 124.

NLP engine 132 may package data identifying the particular service, theparticular product, and in some instances, the particular data type ordata class (e.g., a “responsive” data type or data class) intocorresponding portions of contextual information 210B, and NLP engine132 may provide linguistic element data 210A and contextual information210B as inputs to a predictive engine 134. In some instances, predictiveengine 134 may be configured to accept input data having a particularstructure or format (e.g., “encoded” input data), and NLP engine 132 maybe configured to generate structured portions of linguistic element data210A and contextual information 210B that are consistent with, andcompatible with, the particular structure or format of the encoded inputdata. Upon execution by first computing system 122 (e.g., based on aprogrammatic command generated by executed first chatbot engine 124),predictive engine 134 may perform any of the exemplary processesdescribed herein to determine a capability of first chatbot engine 124to generate a response to additional message 204 based on locallyaccessible data, e.g., as maintained within data repository 126.

For example, contextual information 210B may specify that the inquirywithin additional message 204 corresponds to a balance inquiry for aproduct (e.g., a credit card account) provided to user 101 bythird-party financial institution unrelated to the financial institution(or business unit) that operates first computing system 122. Based onportions of contextual information 210B, predictive engine 134 mayperform any of the exemplary processes described herein to establishthat elements of data responsive to additional message 204 (e.g.,account or transaction data associated with the third-party credit cardaccount) are inaccessible to executed first chatbot engine 124, e.g.,based on data record 135A of capability database 126B, or to executedsecond chatbot engine 154, e.g., based on data record 135B of capabilitydatabase 126B. As such, predictive engine 134 may determine thatexecuted first chatbot engine 124 and executed second chatbot engine 154are each incapable of generating a response appropriate to the balanceinquiry regarding the third-party credit card account.

Further, and using any of the exemplary processes described herein,predictive engine 134 may perform operations that identify an additionalcomputing system that operates within environment 100 and that maintainsthe account or transaction data responsive to the balance inquiryassociated with the third-party credit card account. As illustrated inFIG. 2B, predictive engine 134 may access the data records of capabilitydatabase 126B, e.g., as maintained within data repository 126, andidentify one or more data records, such as data record 212, that includeor reference the unrelated product or service associated with theadditional message 204 (e.g., the balance inquiry associated with thethird-party credit card account), the third-party financial institutionthat provides the unrelated products or services, or the elements ofdata responsive to additional message 204 (e.g., account ortransactional data associated with the third-party credit card account).In some instances, and as described herein, predictive engine 134 mayextract information identifying the unrelated product or service, thethird-party financial institution or business entity that provides theunrelated products or services and additionally, or alternatively, theelements of responsive data from corresponding portions of contextualinformation 210B.

By way of example, and based on data record 212, predictive engine 134may determine that a third party computing system 242 operating withinenvironment 100 locally maintains account or transaction datacharacterizing the credit card account held by user 101 and issued bythe third-party financial institution (e.g., the third-party credit cardaccount) and as such, that a chatbot engine executed by third-partycomputing system 242 (e.g., third-party chatbot engine 244) is capableof responding to the balance inquiry specified within additional message204. In some instances, third-party computing system 242 may beassociated with, or operated by, the unrelated, third-party financialinstitution that issues the third-party credit card account to user 101,and may maintain, or may access (e.g., within a cloud-based repository),elements of sensitive customer, account, or transaction data that enablethird-party chatbot engine 244 to generate a response appropriate to thebalance inquiry specified within additional message 204.

For instance, third-party computing system 242 may represent a computingsystem that includes one or more servers and tangible, non-transitorymemory devices storing executable code and application modules, such as,but not limited to, third-party chatbot engine 244. As described herein,the one or more servers may each include one or more processors, whichmay be configured to execute portions of the stored code or applicationmodules to perform operations consistent with the disclosed embodiments.In some instances, third-party computing system 242 may can beincorporated into a single computing system, although in otherinstances, third-party computing system 242 can correspond to adistributed system that includes computing components distributed acrossone or more communications networks, such as those described herein, orthose provided or maintained by cloud-service providers (e.g., GoogleCloud™, Microsoft Azure™, etc.). The disclosed embodiments are, however,not limited to these exemplary distributed systems, and in otherinstances, third-party computing system 242 may include computingcomponents disposed within any additional or alternate number or type ofcomputing systems or across any appropriate network.

Referring back to FIG. 2B, predictive engine 134 may parse data record212 and extract a unique system identifier 214 of third-party computingsystem 242, such as an IP address or a MAC address, and provideextracted system identifier 214 (and linguistic element data 210A andcontextual information 210B), as inputs to augmentation module 136. Whenexecuted (e.g., via a programmatic command generated by first chatbotengine 124), augmentation module 136 may perform any of the exemplaryprocesses described herein to augment the established chatbot session toinclude third-party chatbot engine 244 that, when executed bythird-party computing system 242, generates messaging data responsive tothe inquiry specified within additional message 204 for provisioningwithin chatbot interface 182.

Executed augmentation module 136 may receive system identifier 214,along with linguistic element data 210A and contextual information 210B,and may perform any of the exemplary processes described herein toestablish an existence of a relationship, or a lack thereof, betweenfirst computing system 122 and third-party computing system 242. Forexample, and as described herein, augmentation module 136 may parse thedata records of relationship database 126C, and identify a correspondingone of the data records, e.g., data record 216, that includes orreferences system identifier 214. Augmentation module 136 may processdata record 216 to access and extract relationship data 218, whichcharacterizes the existence and/or scope of the relationship betweenfirst computing system 122 and third-party computing system 242.Further, and based on all or a portion of relationship data 218,augmentation module 136 may establish the existence, or lack thereof, ofthe relationship between first computing system 122 and third-partycomputing system 242.

By way of example, first computing system 122 may be associated with oroperated by a financial institution that provides retail and investmentbanking services to user 101, and third-party computing system 242 maybe associated with or operated by a third-party financial institutionthat issued a credit card account to user 101. As such, extractedrelationship data 218 may specify that none of the exemplaryrelationships described herein exist between first computing system 122and third-party computing system 242, and augmentation module 136 mayestablish that first computing system 122 and third-party computingsystem 242 are unrelated, and that third-party computing system 242 isincapable of relying on the prior authentication of user 101 by firstcomputing system 122 during any augmentation of the established chatbotsession between chatbot application 104 and first chatbot engine 124upon.

Responsive to the absence of the relationship, augmentation module 136may further parse relationship data 218 to establish a presence, orlack, or consent data 220 indicative of a successful outcome of one ormore of the token-based authentication and consent processes describedherein, such as, but not limited to, an OAuth protocol collectivelyimplemented by application programs executed by client device 102, firstcomputing system 122, and third-party computing system 242. By way ofexample, consent data 220 may include a digital token, cryptogram, hashvalue, or other element of cryptographic data, such as OAuth token 222,which may be indicative of a successful authentication of the identityof user 101 during the implemented OAuth protocol. Further, and asdescribed herein, OAuth token 222 may also confirm that user 101 notonly granted executed first chatbot engine 124 permission to access aprogrammatic interface associated with third-party chatbot engine 244,but also consented to an augmentation of the established chatbot sessionto include third-party chatbot engine 244 and to a provisioning ofmessage data that includes elements of sensitive customer, account, ortransaction data maintained by third-party computing system 242 duringthe augmented chatbot session, e.g., in response to an augmentationrequest programmatically generated by executed first chatbot engine 124.As described herein, one or more application programs executed bythird-party computing system 242 may generate OAuth token 222 inresponse the successful outcome of the implemented OAuth protocols, andmay provision OAuth token 222 to first computing system 122, e.g., forstorage within data record 218.

As illustrated in FIG. 2B, augmentation module 136 may parserelationship data 218 and detect a presence of OAuth token 222 (e.g.,within consent data 220), which indicates the successful outcome of theauthentication and consent processes of the OAuth protocol collectivelyimplemented by application programs executed by client device 102, firstcomputing system 122, and third-party computing system 242. Augmentationmodule 136 may also perform operations that generate elements of data,e.g., baseline session data 224, that identify and characterize theon-going chatbot session established between chatbot application 104(e.g., as executed by client device 102) and first chatbot engine 124(e.g., as executed by first computing system 122). For example, thegenerated elements of baseline session data 224 may include, but are notlimited to: session identifier 176; information that uniquely identifiesclient device 102 and/or executed chatbot application 104 (e.g., anetwork address of client device 102, such as an IP address, anidentifier or address of a programmatic interface of executed chatbotapplication 104, etc.); raw message data characterizing the inquiryspecified within additional message 204 (e.g., a portion of textual data208); and additionally, or alternatively, portions of linguistic elementdata 210A and contextual information 210B, which specify the discretelinguistic elements included within additional message 204 and theestablished context or meaning of the combination of the discretelinguistic elements. In some instances, augmentation module 136 mayperform operations that package OAuth token 222 and baseline sessiondata 224 into corresponding portions of an augmentation request 226,which first computing system 122 may transmit to third-party computingsystem 242 across communications network 120, e.g., using anyappropriate communications protocol.

In other instances, not illustrated in FIG. 2B, augmentation module 136may not detect OAuth token 222, or another element of consent data 220,within a portion of relationship data 218. Responsive to the absence ofOAuth token 222, or to the lack of other elements of consent data 220,augmentation module 136 may perform operations that, either alone or inconjunction with other application programs executed by first computingsystem 122, generate an access request to initiate the one or moretoken-based authentication and consent processes described herein, suchas the OAuth protocol. The access request may, for example, include datathat uniquely identifies user 101 (e.g., a login credential, a digitalidentifier within an open-banking environment, etc.) or client device102 (e.g., an IP address or a MAC address), along with additional datathat uniquely identifies first computing system 122 or executed firstchatbot engine 124 (e.g., an IP address of first computing system 122,an application cryptogram generated by or assigned to executed firstchatbot engine 124, etc.). The access request may also include, but isnot limited to, additional data that characterizes the requested access,such as session identifier 176 of the existing chatbot session orinformation specifying the requested augmentation of the existingchatbot session or data response to the additional inquiry, e.g., theelements of sensitive customer, account, or transaction data maintainedby third-party computing system 242.

First computing system 122 may transmit the access request acrosscommunications network 120 to a programmatic interface associated withthird-party chatbot engine 244 (not illustrated in FIG. 2B). The receiptof the access request at the programmatic interface of third-partychatbot engine 244 may cause third-party computing system 242 to performoperations that, in conjunction with one or more application programsexecuted by client device 102, implement the requested, token-basedauthentication and consent processes, such as the OAuth protocol (notillustrated in FIG. 2B). As described herein, and based on a successfuloutcome of the requested token-based authentication and consentprocesses (e.g., the OAuth protocol), third-party computing system 242may generate elements of consent data 220, such as, but not limited to,OAuth token 222, which confirms the successful authentication of theidentity of user 101, that user 101 granted executed first chatbotengine 124 permission to access a programmatic interface associated withthird-party chatbot engine 244, and that user 101 consented to theaugmentation of the established chatbot session to include third-partychatbot engine 244 and to the provisioning of message data that includeselements of sensitive customer, account, or transaction data maintainedby third-party computing system 242 during the augmented chatbotsession.

In some instances, not illustrated in FIG. 2B, third-party computingsystem 242 may transmit OAuth token 222 (or the elements of consent data220) across communications network 120 to first computing system 122,which may store OAuth token 222 (or the elements of consent data 220)within a corresponding portion of relationship data 218. As describedherein, augmentation module 136 may package OAuth token 222 (or theelements of consent data 220) and baseline session data 224 intocorresponding portions of augmentation request 226, which firstcomputing system 122 may transmit to the programmatic interface ofthird-party chatbot engine 244, e.g., across communications network 120using any appropriate communications protocol.

Referring back to FIG. 2B, the programmatic interface of third-partychatbot engine 244, e.g., application programming interface (API) 246,may receive augmentation request 226. In some instances, API 246 mayparse augmentation request 226 and determine whether a structure orcomposition of OAuth token 222 (or other elements of consent data 220)is consistent with, or corresponds to, a predetermined token structureor composition (e.g., an “expected” token structure or composition). Insome instances, second computing system 152 may store datacharacterizing the expected token structure or composition within one ormore tangible, non-transitory memories, e.g., within a portion of a datarepository 248 accessible to API 246 and third-party chatbot engine 244.

If, for example, API 246 were to detect an inconsistency between theexpected token structure or composition and the structure or compositionof OAuth token 222 (or the other elements of consent data 220), API 246may decline to establish communications with executed first chatbotengine 124, and third-party computing system 242 may generate andtransmit an error message indicative of the detected inconsistency tofirst computing system 122, e.g., across communications network 120using any appropriate communications protocol. In other instances, ifAPI 246 were to establish a consistency between the expected tokenstructure or composition and the structure or composition of OAuth token222 (or the other elements of consent data 220), API 246 may establishthat first chatbot engine 124 is permitted to access third-party chatbotengine 244, and may establish secure, programmatic communicationsbetween first chatbot engine 124 (e.g., as executed by first computingsystem 122) and third-party chatbot engine 244 (e.g., as executed bythird-party computing system 242).

Response to the established permission, API 246 may route augmentationrequest 226 to a security module 250 of third-party chatbot engine 244,which may perform operations that validate OAuth token 222 (or the otherelements of consent data 220). By way of example, when executed bythird-party computing system 242 (e.g., based one or more commandsprogrammatically generated by executed third-party chatbot engine 244),security module 250 may perform operations that compare OAuth token 222against a local OAuth token 251 maintained within the one or moretangible, non-transitory memories, e.g., within session database 248A ofdata repository 248. In some instance, local OAuth token 251 may beassociated with, or linked to, the unique identifier of user 101, clientdevice 102, first computing system 122, and/or first chatbot engine 124,portions of which may be included within baseline session data 224.

If security module 250 were to detect an inconsistency between OAuthtoken 222 and local OAuth token 251, security module 250 may decline toaugment the existing chatbot session between executed chatbotapplication 104 and executed first chatbot engine 124. Responsive to thedetected inconsistency, executed third-party chatbot engine 244 maygenerate and transmit an error message indicative of the failedaugmentation process to first computing system 122, which may causeexecuted first chatbot engine 124 to perform any of the exemplaryprocesses described herein to select another computing system capable ofresponding to the inquiry specified within additional message 192 and/orto prompt user 101 to obtain information responsive to the inquirythrough out-of-session channels.

Alternatively, if security module 250 were to establish a consistencybetween OAuth token 222 and local OAuth token 251, security module 250may validate OAuth token 222 and rely on the confirmed authenticationand consent (e.g., as indicated by now-validated OAuth token 222) whenestablishing a secure, programmatic communications session with chatbotapplication 104 executed by client device 102. In response to thesuccessful validation of OAuth token 222, executed third-party chatbotengine 244 may perform operations that establish a handshake between API246 and a communications interface of client device 102, whichfacilitates secure, programmatic communications and exchanges of databetween third-party chatbot engine 244 and chatbot application 104, andwhich augments the existing chatbot session to include messaging datagenerated by third-party chatbot engine 244.

Security module 250 may also perform operations that store augmentationrequest 226, including baseline session data 224, within one or moredata records of session database 248A associated with the existingchatbot session and now-validated OAuth token 222. Further, andresponsive to the validation of OAuth token 222, security module 250 mayextract baseline session data 224 from augmentation request 226, and mayroute baseline session data 224 to one or more additional applicationmodules of third-party chatbot engine 244 which, upon execution, performany of the exemplary processes described herein to generate a responseto the inquiry specified within additional message 204 (e.g., therequest for the balance of the third-party credit card account) based onportions of baseline session data 224, to programmatically augment theestablished chatbot session between executed chatbot application 104 andexecuted first chatbot engine 124 to include third-party chatbot engine244, and to provision the provision the generated response forpresentation within chatbot interface 182 during the augmented chatbotsession.

By way of example, as illustrated in FIG. 2B, security module 250 mayroute baseline session data 224 to a response generation module 252 ofthird-party chatbot engine 244. As described herein, baseline sessiondata 224 may include a unique identifier of user 101 (e.g., a logincredential, etc.), client device 102 (e.g., a network address, such asan IP address, etc.), or chatbot application 104 (e.g., an address of acorresponding programmatic interface, etc.) associated with additionalmessage 192. Further, as also described herein, baseline session data224 may also associate each of the unique identifiers with additionalinformation that characterizes the inquiry specified within additionalmessage 204, such as, but not limited to, all or a portion of textualdata 208, linguistic element data 210A and additionally, oralternatively, contextual information 210B.

When executed by third-party chatbot engine 244 (e.g., based on one ormore programmatically generated commands), response generation module252 may parse baseline session data 144 to extract one or more of theunique identifiers of user 101, client device 102, and/or executedchatbot application 104, and all or a portion of contextual data 210B,which characterizes the inquiry associated with additional message 204,the product or service specified by that inquiry, and further, specificelements would facilitate a generation of a response to the inquiry. Insome instances, response generation module 252 may access one or moreelements of the sensitive customer, account, or transaction data (e.g.,as maintained within data repository 248) that are consistent with theextracted identifiers and the extracted portion of contextualinformation 210B, and based on the accessed customer, account, ortransaction data, perform any of the exemplary processes describedherein to generate a response to the inquiry specified within additionalmessage 204 and package the generated response into one or more elementsof response data 254.

Further, in some examples, response generation module 252 may alsopackage, into the one or more elements of response data 254, certainportions of baseline session data 224 that identify and characterize theinquiry, such as, but not limited to, an inquiry type (e.g., the balanceinquiry specified within contextual information 210B) and a subject ofthe inquiry (e.g., the third-party credit card account held by user 101,as specified within contextual information 210B). In some instances, thepackaged portions of baseline session data 224 may correspond tometadata that, when associated with the generated response within theone or more elements of response data 254, further describes orcharacterizes the generated response and facilitates a generation ofmessaging data that responds to additional message 204 within anaugmented chatbot session, as described herein.

As described herein, the inquiry specified within additional message 204may correspond to a balance inquiry regarding a third-party credit cardaccount issued by the third-party financial institution to user 101(e.g., “What is the balance on my third-party credit card?”). Using anyof the exemplary processes described herein, response generation module252 may access locally stored baseline session data 224, e.g., asmaintained within the data records of session database 248A, and extracta unique identifier of user 101 (e.g., the login credential of user 101,etc.) and/or a unique identifier of client device 102 (e.g., the IPaddress of client device 102). Response generation module 252 may alsoextract, from accessed baseline session data 224, a portion ofcontextual information 210B that characterizes the inquiry as a balanceinquiry and that identifies locally maintained cred-card account data asfacilitating a response to the balance inquiry.

In some instances, illustrated in FIG. 2B, response generation module164 may access account database 248B, e.g., as maintained within datarepository 248, and identify one or more data records 256 that includeor reference the unique identifier of user 101 or client device 102. Forexample, accessed data records 256 may include a current balance of thethird-party credit card account held by user 101 (e.g., $1,375.00), andresponse generation module 252 may perform operations that extract thecurrent balance from accessed data records 256. In other instances,accessed data records 256 may identify one or more sequentially orderedtransactions involving the third-party credit card account during one ormore temporal intervals, and response generation module 252 may computethe current balance of the investment account based on the one or moresequentially ordered transactions within accessed data records 256.Response generation module 164 may also perform operations that packagethe extracted or computed current balance of the third-party credit cardaccount within the one or more elements of response data 254, along withadditional portions of contextual information 210B that identify andcharacterize the balance inquiry and the third-party credit cardaccount.

As described herein, response generation module 252 may generate all, ora portion, of response data 254 based a contextual analysis ofadditional message 204 performed by executed first chatbot engine 124,e.g., based on contextual information 210B derived from an applicationof one or more of the natural language processing techniques to textualdata 110 by NLP engine 132 of executed first chatbot engine 124. Inother exemplary embodiments, third-party chatbot engine 244 may alsoinclude one or more local natural language processing (NLP) modules (notillustrated in FIG. 2B), which, in response to a successful validationof OAuth token 222, apply one or more of the natural language processing(NLP) processes or algorithms described herein to all or a portion oftextual data 208 included within baseline session data 224. Based on theapplication of these NLP processes or algorithms, the one or more localNLP modules of third-party chatbot engine 244 may perform any of theexemplary processes described herein to identify the one or morediscrete linguistic elements within textual data 208, and to generatelocal contextual information that establishes the context and meaning ofcombinations of the discrete linguistic elements within textual data208, each of which may be provided as inputs to response generationmodule 252.

Referring back to FIG. 2B, response generation module 252 may provideresponse data 254 as an input to a messaging module 258 of third-partychatbot engine 244. In some instances, messaging module 258 may performany of the exemplary processes described herein to generate a responsemessage 260 that includes one or more selected portions of response data254, is responsive to the inquiry specified within additional message204, and when presented within chatbot interface 182, facilitates anongoing and simulated conversation between user 101 and the chatbotgenerated or hosted programmatically by first chatbot engine 124 andthird-party chatbot engine 244 during the augmented chatbot session.

For example, response message 260 may indicate that the third-partycredit card account is characterized by a current balance of $1,375.00,and may include additional information, e.g., metadata, that identifiesthe specified inquiry as a balance inquiry (e.g., the inquiry type)associated with the third-party credit card account held by user 101(e.g., the subject of the inquiry). Messaging module 258 may parseresponse data 254 to extract the current balance $1,375.00 and theelements of metadata (e.g., that identify the inquiry type and thesubject of the inquiry). Further, using any of the exemplary text ormessage generation processes described herein, messaging module 258 maygenerate elements of textual content 262 that are consistent withextracted elements of metadata and identify the extracted currentbalance of the third-party credit card account. In some instances, whenrendered for presentation within chatbot interface 182, the generatedelements of textual content may correspond to, and represent, a responseto the additional message 204 within the ongoing and facilitatedconversation between the between user 101 and the chatbots generated orhosted programmatically by first chatbot engine 124 and third-partychatbot engine 244, e.g., during the augmented chatbot session describedherein.

As illustrated in FIG. 2B, messaging module 258 may perform operationsthat package textual content 262 into a corresponding portion ofresponse message 260. Messaging module 2858 may also perform operationsthat package, into additional portions of response message 260,information that includes, but is not limited to session identifier 176,which messaging module 258 may extract from a corresponding portion oflocally maintained baseline session data 224 (e.g., within the datarecords of session database 248A), and data 264 that identifies orcharacterizes third-party chatbot engine 244 or third-party computingsystem 242. In some instances, when processed by executed chatbotapplication 104, session identifier 176 and/or data 264 causes executedchatbot application 104 to establish a secure, programmatic channel ofcommunications with executed third-party chatbot engine 244, and toinstantiate an augmented chatbot session that facilitates an ongoing andsimulated conversation between user 101, executed first chatbot engine124, and executed third-party chatbot session 244. Messaging module 258may perform operations that cause third-party computing system 242 totransmit response message 260 across communications network 120 toclient device 102.

Referring to FIG. 2C, client device 102 may receive response message260, and API 105 may route response message 260 to session augmentationmodule 116 of chatbot application 104. Upon execution by chatbotapplication 104, session augmentation module 116 may receive responsemessage 260 and may perform operations that parse response message 260to extract session identifier 176 and that access local session data 115and obtain local session identifier 117. As described herein, localsession identifier 117 which corresponds to the unique alphanumericsession identifier received by executed chatbot application 104 fromexecuted first chatbot engine 124 upon initiation of the establishedchatbot session.

Session augmentation module 116 may perform operations that determinewhether local session identifier 117 is consistent with, and correspondsto, extracted session identifier 176. If, for example, sessionaugmentation module 116 were to detect an inconsistency between localsession identifier 117 and extracted session identifier 176, sessionaugmentation module 116 may decline to establish a secure, programmaticchannel of communications between executed chatbot application 104 andexecuted third-party chatbot engine 244, and may further decline toaugment the existing chatbot session to include third-party chatbotengine 244. Alternatively, if session augmentation module 116 were toestablish a consistency between local session identifier 117 andextracted session identifier 176, session augmentation module 116 mayperform any of the exemplary processes described herein to establish thesecure, programmatic channel of communications between executed chatbotapplication 104 and executed third-party chatbot engine 244 (e.g., viaAPI 105 of executed chatbot application 104 and API 246 of executedthird-party chatbot engine 244), and to instantiate the augmentedchatbot session that facilitates the ongoing and simulated conversationbetween user 101, executed first chatbot engine 124, and executedthird-party chatbot session 244.

For example, and responsive to the established consistency, sessionaugmentation module 116 may provide response message 260 as an input tochatbot interface module 118, which may perform any of the exemplaryprocesses described herein to extract textual content 262 from responsemessage 260, and to generate one or more interface elements 265representative of textual content 262. In some instances, chatbotinterface module 118 may provide interface elements 265 to a displayunit of client device 102 (not illustrated in FIG. 2C), which maypresent interface elements 265 within a corresponding portion of chatbotinterface 182 during the now-augmented chatbot session, e.g., asresponse 266 indicating that “The current balance of your third-partycredit card account is $1,375.00.”

FIG. 3 is a flowchart of an exemplary process 300 for dynamicallyaugmenting and cryptographically secure augmentation of aprogrammatically generated chatbot session, in accordance with thedisclosed embodiments. In some examples, a network-connected computingsystem, such as first computing system 122 of FIGS. 1A-1C and 2A-2C, mayperform one or more of the exemplary steps of process 300, whichestablish a secure, programmatic communication session (e.g., a chatbotsession) with a chatbot application executed by a network-connecteddevice, such as client device 102 of FIGS. 1A-1C and 2A-2C, and whichdynamically augment that established chatbot session based on secure,programmatic communications established initiated with applicationprograms executed by one or more additional network-connected computingsystems, such as second chatbot engine 154 executed by second computingsystem 152 of FIGS. 1A-1C or third-party chatbot engine 244 executed bythird-party computing system 242 of FIGS. 2A-2C.

Referring to FIG. 3, first computing system 122 may perform operationsthat establish a secure, programmatic communications session, e.g., achatbot session, with the chatbot application executed at client device102 (e.g., in step 302). By way of example, and as described herein,first computing system 122 may receive a request to initiate the chatbotsession from the executed chatbot application, e.g., executed chatbotapplication 104. Based on portions of the received request, firstcomputing system 122 may perform any of the exemplary processesdescribed herein to verify an authenticity of the request and further,to confirm a prior authentication of an identity of user 101 by clientdevice 102. In response to a successful outcome of these exemplaryverification and confirmation operations, first computing system mayperform any of the exemplary processes described herein to initiate thechatbot session with executed chatbot application 104, and to generateand transmit an initial, introductory message across a correspondingcommunications network to client device 102. As described herein,executed chatbot application 104 may perform operations that presentinterface elements representative of the initial, introductory messagewithin a digital interface associated with the now-established chatbotsession, e.g., chatbot interface 182.

In some instances, first computing system 122 may receive messaging datagenerated by executed chatbot application 104 during the establishedchatbot session (e.g., in step 304). As described herein, executedchatbot application may generate the messaging data based on inputprovided to client device 102 by user 101, e.g., via a correspondinginput unit and in response to a presentation of the initial,introductory message within chatbot interface 182. For example, theinput provided by user 101 may include an inquiry posed by user 101during the existing chatbot session, the inquiry may reference a productor service provided or offered by the financial institution associatedfirst computing system 122, a business unit of that financialinstitution, one or more related financial institutions or businessentities, or a third-party financial institution unrelated to the firstcomputing system 122.

As described herein, the one or more received elements of messaging datamay include textual content that characterizes the inquiry, along with aunique identifier of one or more of user 101 (e.g., an alphanumericauthentication credential, a digital identifier, etc.), of client device102 (e.g., a network address, such as an IP address, a MAC address,etc.), or of executed chatbot application 104 (e.g., an applicationcryptogram, etc.). In some instances, first computing system 122 mayparse the one or more received elements of messaging data to extract thetextual content representative of the inquiry (e.g., in step 306). Firstcomputing system 122 may also perform operations that apply one or moreof the NLP algorithms or processes described herein to all, or aselected portion, of the extracted textual content (e.g., in step 308).Further, and based on the application of the one or more NLP algorithmsor processes, first computing system 122 may perform any of theexemplary processes described herein to identify one or more discretelinguistic elements (e.g., a word, a combination of morphemes, a singlemorpheme, etc.) within the extracted textual data, and to establish acontext and a meaning of combinations of the discrete linguisticelements (e.g., also in step 308).

As described herein, the established context or meaning of thecombination of the discrete linguistic elements may, for example,identify a product or service associated with or requested by the one ormore elements of received messaging data, e.g., as specified by theinquiry within extracted textual content, and additionally, oralternatively, may identify a particular data type or data class thatwould be responsive to that inquiry, e.g., a responsive data type orclass. In some instances, and based on a portion of the discretelinguistic elements, on information characterizing the identifiedproduct or service and additionally, or alternatively, on informationcharacterizing the responsive data type or class, first computing system122 may perform any of the exemplary processes described herein todetermine its capability to generate a response to the messaging databased on locally accessible data (e.g., in step 310).

By way of example, and as described herein, first computing system 122may be associated with, or operated by, a financial institution (or aparticular business unit of that financial institution, such as a retailbanking unit) that provides certain products or related services to user101, and as such, may maintain sensitive customer, account, ortransaction data that characterizes the provided products or relatedservices within one or more tangible, non-transitory memories. In someinstances, in step 310, first computing system 122 may perform any ofthe exemplary processes described herein to determine that firstcomputing system 122 provides the product or service identified withinthe messaging data, or that the locally maintained elements of sensitivecustomer, account, or transaction data correspond to the responsive datatype or data class. Responsive to this determination, first computingsystem 122 may establish in step 310 the capability to generate theresponse to the messaging data based on the locally maintained oraccessible elements of sensitive customer, account, or transaction data.

In response to the determination that first computing system 122 iscapable of generating the response to the messaging data (e.g., step310; YES), first computing system 122 may perform any of the exemplaryprocesses described herein to generate a response to the inquiryspecified within the received messaging data based on selected portionsof the locally maintained elements of sensitive customer, account, ortransaction data (e.g., in step 312). First computing system 122 maytransmit the generated response across communications network 120 toclient device 102 (e.g., in step 314). As described herein, aprogrammatic interface associated with executed chatbot application 104,e.g., API 105, may receive the generated response, and executed chatbotapplication 104 may perform any of the exemplary processes describedherein to generate and present interface elements representative of thereceived response within a corresponding digital interface, e.g.,chatbot interface 182 of FIGS. 1A-1C and 2A-2C.

Referring back to FIG. 3, first computing system 122 may determine instep 316 whether executed chatbot application 104 elects to maintain apendency of the established chatbot session, e.g., subsequent to orresponsive to the presentation of the response within the chatbotinterface 182. For example, and upon the presentation of response withinthe chatbot interface 182 by executed chatbot application 104, user 101may provide additional input to client device 102 that requests atermination of the established chatbot session. Responsive to theadditional input, executed chatbot application 104 may generate arequest to terminate the chatbot session (e.g., as established in step302, above), and client device 102 may transmit the termination requestacross communications network 120 to first computing system 122. In someinstances, and in response to the termination request, first computingsystem 122 may perform operations that terminate the secure,programmatic communications session with executed chatbot application104 and as such, that terminate the established chatbot session.

If first computing system 122 were to determine that chatbot application104 elected to terminate the established chatbot session (step 316; NO),exemplary process 300 may be complete in step 318. Alternatively, iffirst computing system 122 were to determine that chatbot application104 elected to maintain the pendency of the established chatbot session(step 316; YES), exemplary process 300 may pass back to step 304, andfirst computing system 122 may perform operations that await a receiptof additional messaging data during the established chatbot session.

Referring back to step 310, first computing system 122 may determinethat the provisioned products or services are inconsistent with theproduct or service identified within the one or more elements ofmessaging data and additionally, or alternatively, that the locallymaintained elements of sensitive customer, account, or transaction dataare inconsistent with the responsive data type or data class. Based onthis determination, first computing system 122 may determine that it isincapable of responding to the inquiry specified messaging data (e.g.,step 310; NO), and first computing system 122 may perform any of theexemplary processes described herein to identify an additional computingsystem that is capable of responding to the specified inquiry (e.g., instep 320).

By way of example, in step 324, first computing system may access one ormore data records of a capability database (e.g., capability database126B of FIGS. 1B and 2B), each of which include identification data thatcharacterize a corresponding computing system (e.g., second computingsystem 152, third-party computing system 242, etc.) and a chatbot engineexecuted by the corresponding computing system (e.g., second chatbotengine 154, third-party chatbot engine 244, etc.). Further, and asdescribed herein, the data record associated with each of thecorresponding computing systems may also link the identification data tocapability data that specifies products or services provided by thecorresponding computing system and/or data types or classes available tothe corresponding one of the executed chatbot engines.

Based on portions of the linked capability data, first computing system122 may perform any of the exemplary processes described herein toidentify the additional computing system capable of responding to theinquiry within the messaging data, and to extract all or a portion ofthe identification data associated with the additional computing system(e.g., also in step 320). Further, first computing system 122 mayperform any of the exemplary processes described herein to establish anexistence, or absence, of a relationship between first computing system122 and the now-identified identified additional computing system (e.g.,in step 322).

In one instance, first computing system 122 may perform any of theexemplary processes described herein to establish the existence of therelationship with the additional computing system (e.g., step 322; YES).As described herein, the established existence of the relationship mayenable a chatbot engine executed by the additional computing system toestablish a secure communications session with executed chatbotapplication 104 based on a prior authentication between executed chatbotapplication 104 and first computing system 122. The prior authenticationbetween executed chatbot application 104 and first computing system 122may also confirm that user 101 consented to an access of sensitivecustomer, account, or transaction data by first computing system 122,and in some instances, the existence of that relationship may imply thatthe chatbot engine executed by the additional computing system may relyon the provided consent to access similarly sensitive customer, account,or transaction data maintained locally at the additional computingsystem.

Responsive to the established relationship, first computing system 122may perform any of the exemplary processes described herein generate adigital authentication token representative of that prior authenticationand/or consent of user 101 (e.g., in step 324). For example, firstcomputing system 122 may generate the digital authentication token basedon an application of one or more token-generation algorithms orprocesses to input data that includes, but is not limited to, anidentifier of client device 102 and/or executed chatbot application 104(e.g., a network address of client device 102, such as an IP address, anidentifier or address of a programmatic interface of executed chatbotapplication 104, etc.), a portion of the received messaging data (e.g.,that characterizes the specified inquiry), and additionally, oralternatively, portions of generated linguistic element data andcontextual information generated through the application of the NLPalgorithms or techniques described herein. In some instances, thedigital authentication token may be characterized by a structurespecified by, or recognized by, the chatbot engine executed by theadditional computing system and/or a programmatic interface of thatexecuted chatbot engine, and examples of the digital authenticationtoken include, but are not limited to, a cryptogram, a hash, randomnumber, or other element of cryptographic data having a predeterminedlength or structure.

In some instances, first computing system 122 may perform operationsthat package the digital authentication token into a portion of anaugmentation request, along with elements of baseline session data thatidentify and characterize the on-going chatbot session establishedbetween chatbot application 104 and first computing system 122 (e.g., instep 326). In some instances, the generated elements of baseline sessiondata may include, but are not limited to: the unique alphanumericidentifier of the established chatbot session; information that uniquelyidentifies client device 102 and/or executed chatbot application 104(e.g., a network address of client device 102, such as an IP address, anidentifier or address of a programmatic interface of executed chatbotapplication 104, etc.); raw message data characterizing the inquirywithin the messaging data; and additionally, or alternatively, portionsof the linguistic element data and contextual information describedherein. In some instances, first computing system 122 may transmit theaugmentation request to the additional computing system acrosscommunications network 120 using any appropriate communications protocol(e.g., in step 328). The additional computing system can perform any ofthe exemplary processes described herein to verify the digitalauthentication token, and responsive to a successful verification,generate an appropriate response to the specified inquiry based on thelocally accessible elements of sensitive customer, account, ortransaction data, augment the established chatbot session byestablishing secure, programmatic communications session with executedchatbot application 104, and provision the generated response to clientdevice 102 for presentation on chatbot interface 182 during theaugmented chatbot session.

Exemplary process 300 may then pass back to step 316, and firstcomputing system 122 may perform operations that determine whetherexecuted chatbot application 104 elects to maintain the establishedchatbot session e.g., subsequent to the provisioning of the responsegenerated by additional computing system within chatbot interface 182.

In some exemplary embodiments, described herein in reference to steps328-328, first computing system 122 and the additional computing systemmay perform operations that augment an established chatbot session basedon, among other things, a determined existence of a relationship betweenfirst computing system 122 and the additional computing system, and aprior authentication of the identity of user 101 by first computingsystem 122 during the established chatbot session. In other instances,and referring back to step 322, first computing system 122 may performany of the exemplary processes described herein determine that theadditional computing system is unrelated to the financial institutionthat operates first computing system 122 (e.g., step 322; NO). Based onthe determined absence of the relationship, first computing system 122and the additional computing system may be unable to rely on the priorauthentication of user 101, or the prior grant of consent by user 101,to augment the established chatbot session. In other exemplaryembodiments, and in view of the determined absence of the relationship,first computing system 122 and the additional computing system mayperform operations that augment the established chatbot session basedon, among other things, data indicative of a successful outcome of oneor more token-based authentication and consent processes, such as, butnot limited to, the OAuth protocol described herein.

For instance, in step 324, first computing system 122 may perform any ofthe exemplary processes described herein to obtain consent dataindicative of a successful outcome of one or more token-basedauthentication and consent processes, e.g., the OAuth protocolimplemented in conjunction with the additional computing system andclient device 102 (e.g., in step 330). The consent data may, forexample, include a digital token, cryptogram, hash value, or otherelement of cryptographic data, such as an OAuth token indicative of asuccessful authentication of the identity of user 101, and of an accesspermission granted by user 101, during the implemented OAuth protocol.For example, in step 330, first computing system 122 may extract theconsent data associated with the additional computing system, e.g., theOAuth token, from one or more tangible, non-transitory memories. Inother instances, also in step 330, first computing system 122 mayperform operations that, in conjunction with application programsexecuted by the additional computing system and client device 102,initiate the one or more token-based authentication and consentprocesses, e.g., the OAuth protocol, and in response to a successfuloutcome, that provision the consent data, e.g., the OAuth token, tofirst computing system 122.

In some instances, first computing system 122 may perform operationsthat package the consent data, e.g., the OAuth token, into a portion ofan augmentation request, along with elements of baseline session datathat identify and characterize the established chatbot session (e.g., instep 332), and that transmit the augmentation request to the additionalcomputing system across communications network 120 using any appropriatecommunications protocol (e.g., in step 334). The additional computingsystem can perform any of the exemplary processes described herein toverify the consent data (e.g., the OAuth token), and responsive to asuccessful verification, generate an appropriate response to thespecified inquiry based on the locally accessible elements of sensitivecustomer, account, or transaction data, augment the established chatbotsession by establishing secure, programmatic communications session withexecuted chatbot application 104, and provision the generated responseto client device 102 for presentation on chatbot interface 182 duringthe augmented chatbot session.

Exemplary process 300 may then pass back to step 316, and firstcomputing system 122 may perform operations that determine whetherexecuted chatbot application 104 elects to maintain the establishedchatbot session e.g., subsequent to the provisioning of the responsegenerated by additional computing system within chatbot interface 182.

FIG. 4 is a flowchart of an exemplary process 400 for dynamically andsecurely augmenting a programmatically generated chatbot session, inaccordance with the disclosed embodiments. In some examples, anetwork-connected computing system, such as second computing system 152of FIGS. 1A-1C or third-party computing system 242 of FIGS. 2A-2C, mayperform one or more of the exemplary steps of process 400, whichinclude, among other things, dynamically augmenting the establishedchatbot session based on secure, programmatic communications initiatedwith one or more application programs executed by an additionalnetwork-connected device, such as chatbot application 104 executed byclient device 102.

Referring to FIG. 4, a programmatic interface of second computing system152 (or third-party computing system 242) may receive an augmentationrequest generated by an additional computing system, such as, but notlimited to, first computing system 122 of FIGS. 1A-1C and 2A-2C (e.g.,in step 402). By way of example, the received augmentation request mayinclude one or more elements of authentication or consent data, such as,but not limited to, the authentication token or the OAuth tokendescribed herein, along with elements of baseline session data thatidentify and characterize an chatbot session between a chatbot engineexecuted by first computing system 122 (e.g., first chatbot engine 124)and a chatbot application executed by client device 102 (e.g., chatbotapplication 104), and an inquiry specified within messaging datagenerated during that established chatbot session.

As described herein, the authentication token may be indicative of asuccessful prior authentication of user 101 by first computing system122 during the established chatbot session (e.g., based on an existingrelationship between first computing system 122 and second computingsystem 152). Examples of the authentication token include, but are notlimited to, a cryptogram, a hash, random number, or other element ofcryptographic data having a predetermined length or structure (e.g., anumber of leading or trailing zeroes, etc.). Further, the one or moreelements of authentication or consent data may be indicative of asuccessful outcome of the one or more token-based authentication andconsent protocols described herein (e.g., based on a determined lack ofa relationship between first computing system 122 and third-partycomputing system 242). For example, the OAuth token may indicate andconfirm a successful outcome of a collective implementation of the OAuthprotocol described herein by the application programs executed by firstcomputing system 122, third-party computing system 242, and clientdevice 102.

In some instances, second computing system 152 (or third-party computingsystem 242) may perform any of the exemplary processes described hereinto validate the elements of authentication or consent data includedwithin the received augmentation request (e.g., in step 404). Forexample, in step 404, second computing system 152 (or third-partycomputing system 242) may validate the authentication token based on acomparison with a local authentication token extracted from one or moretangible, non-transitory memories, or alternatively, computed using anyof the exemplary processes described herein. In other examples, and asdescribed herein, second computing system 152 (or third-party computingsystem 242) may validate the OAuth token based on a comparison with alocally maintained copy of the OAuth token, e.g., as stored locallywithin the one or more tangible, non-transitory memories.

If second computing system 152 (or third-party computing system 242)were to determine an inconsistency between the received and locallyextracted or computed copies of the authentication or consent data(e.g., the authentication token or the OAuth token), second computingsystem 152 (or third-party computing system 242) may decline to validatethe authentication or consent data (e.g., step 404; NO). In someinstances, second computing system 152 (or third-party computing system242) may generate and transmit an error message indicative of the failedvalidation to first computing system 122 (e.g., in step 406), andexemplary process 400 may then be complete in step 408.

Alternatively, if second computing system 152 (or third-party computingsystem 242) were to establish a consistency between the received andlocally extracted or computed copies of the authentication or consentdata (e.g., the authentication token or the OAuth token), secondcomputing system 152 (or third-party computing system 242) may validatethe received authentication or consent data (e.g., step 404; YES).Responsive to the successful validation of the authentication or consentdata, second computing system 152 (or third-party computing system 242)may perform operations that establish a handshake with a communicationsinterface of client device 102, which facilitates secure, programmaticcommunications and exchanges of data between second computing system 152(or third-party computing system 242) and chatbot application 104executed by client device 102 (e.g., in step 410). Further, in step 410second computing system 152 (or third-party computing system 242) mayalso perform operations that store now-validated the augmentationrequest, including the now-validated authentication or consent data andthe baseline session data, within the one or more tangible,non-transitory memories.

Second computing system 152 (or third-party computing system 242) mayalso extract the baseline session data from the received augmentationrequest, and may perform any of the exemplary processes described hereinto generate a response to the inquiry specified within the baselinesession data (e.g., in step 412). As described herein, the baselinesession data may include a unique identifier of user 101 (e.g., a logincredential, etc.), client device 102 (e.g., a network address, such asan IP address, etc.), or chatbot application 104 (e.g., an address of acorresponding programmatic interface, etc.) associated with establishedchatbot session. Further, as also described herein, baseline sessiondata 144 may also associate each of the unique identifiers withadditional information that characterizes the inquiry specified withinthe additional messaging data, such as, but not limited to, one or morediscrete linguistic elements that correspond to the specified inquiryand contextual data establishing a context or meaning of the specifiedinquiry.

For example, in step 412, second computing system 152 (or third-partycomputing system 242) may parse the baseline session data to extract oneor more of the unique identifiers of user 101, client device 102, and/orexecuted chatbot application 104, and all or a portion of the contextualinformation, which characterizes the inquiry included within the messagedata, the product or service specified by that inquiry, and further,types or classes of data (e.g., responsive data types or classes) thatwould facilitate a generation of a response to the inquiry. In someinstances, second computing system 152 (or third-party computing system242) may access one or more elements of the sensitive customer, account,or transaction data that are consistent with the extracted identifiersand the extracted portion of the contextual data, and perform any of theexemplary processes described herein to generate a response to thespecified inquiry that includes, or is based on, the accessed elementsof the sensitive customer, account, or transaction data (e.g., also instep 412).

Second computing system 152 (or third-party computing system 242) mayperform any of the exemplary processes described herein to transmit thegenerated response across communications network 120 to client device102 (e.g., in step 414). As described herein, a programmatic interfaceof client device 102 (e.g., API 105 of executed chatbot application 104)may receive the generated response, and perform any of the exemplaryprocesses described herein to present one or more interface elementsrepresentative of the generated response within a corresponding portionof chatbot interface 182 during the now-augmented chatbot engine.Exemplary process 400 is then complete in step 416.

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Exemplary embodiments of the subject matterdescribed in this specification, such as, but not limited to, chatbotapplication 104, chatbot engines 124, 154, and 224, APIs 105, 128, 156,and 246, session management module 130, NLP engine 132, predictiveengine 134, augmentation module 136, security modules 160 and 250,response generation modules 164 and 252, messaging modules 168 and 258,session augmentation module 116, and chatbot interface module 118, canbe implemented as one or more computer programs, i.e., one or moremodules of computer program instructions encoded on a tangiblenon-transitory program carrier for execution by, or to control theoperation of, a data processing apparatus (or a computer system).

Additionally, or alternatively, the program instructions can be encodedon an artificially generated propagated signal, such as amachine-generated electrical, optical, or electromagnetic signal that isgenerated to encode information for transmission to suitable receiverapparatus for execution by a data processing apparatus. The computerstorage medium can be a machine-readable storage device, amachine-readable storage substrate, a random or serial access memorydevice, or a combination of one or more of them.

The terms “apparatus,” “device,” and “system” refer to data processinghardware and encompass all kinds of apparatus, devices, and machines forprocessing data, including, by way of example, a programmable processorsuch as a graphical processing unit (GPU) or central processing unit(CPU), a computer, or multiple processors or computers. The apparatus,device, or system can also be or further include special purpose logiccircuitry, such as an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit). The apparatus, device, orsystem can optionally include, in addition to hardware, code thatcreates an execution environment for computer programs, such as codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them.

A computer program, which may also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code, can be written in any form of programming language,including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program may, butneed not, correspond to a file in a file system. A program can be storedin a portion of a file that holds other programs or data, such as one ormore scripts stored in a markup language document, in a single filededicated to the program in question, or in multiple coordinated files,such as files that store one or more modules, sub-programs, or portionsof code. A computer program can be deployed to be executed on onecomputer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, such as an FPGA (field programmable gate array), an ASIC(application-specific integrated circuit), one or more processors, orany other suitable logic.

Computers suitable for the execution of a computer program include, byway of example, general or special purpose microprocessors or both, orany other kind of central processing unit. Generally, a CPU will receiveinstructions and data from a read-only memory or a random-access memoryor both. The essential elements of a computer are a central processingunit for performing or executing instructions and one or more memorydevices for storing instructions and data. Generally, a computer willalso include, or be operatively coupled to receive data from or transferdata to, or both, one or more mass storage devices for storing data,such as magnetic, magneto-optical disks, or optical disks. However, acomputer need not have such devices. Moreover, a computer can beembedded in another device, such as a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storagedevice, such as a universal serial bus (USB) flash drive.

Computer-readable media suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, such as EPROM, EEPROM, and flash memory devices; magneticdisks, such as internal hard disks or removable disks; magneto-opticaldisks; and CD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display unit, such as a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, a TFT display, or an OLED display, fordisplaying information to the user and a keyboard and a pointing device,such as a mouse or a trackball, by which the user can provide input tothe computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback, such as visual feedback,auditory feedback, or tactile feedback; and input from the user can bereceived in any form, including acoustic, speech, or tactile input. Inaddition, a computer can interact with a user by sending documents toand receiving documents from a device that is used by the user; forexample, by sending web pages to a web browser on a user's device inresponse to requests received from the web browser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back-endcomponent, such as a data server, or that includes a middlewarecomponent, such as an application server, or that includes a front-endcomponent, such as a computer having a graphical user interface or a Webbrowser through which a user can interact with an implementation of thesubject matter described in this specification, or any combination ofone or more such back-end, middleware, or front-end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, such as a communication network. Examples ofcommunication networks include a local area network (LAN) and a widearea network (WAN), such as the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someimplementations, a server transmits data, such as an HTML page, to auser device, such as for purposes of displaying data to and receivinguser input from a user interacting with the user device, which acts as aclient. Data generated at the user device, such as a result of the userinteraction, can be received from the user device at the server.

While this specification includes many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments may also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment mayalso be implemented in multiple embodiments separately or in anysuitable sub-combination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination may in some casesbe excised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems maygenerally be integrated together in a single software product orpackaged into multiple software products.

In this application, the use of the singular includes the plural unlessspecifically stated otherwise. In this application, the use of “or”means “and/or” unless stated otherwise. Furthermore, the use of the term“including,” as well as other forms such as “includes” and “included,”is not limiting. In addition, terms such as “element” or “component”encompass both elements and components comprising one unit, and elementsand components that comprise more than one subunit, unless specificallystated otherwise. The section headings used herein are fororganizational purposes only, and are not to be construed as limitingthe described subject matter.

Various embodiments have been described herein with reference to theaccompanying drawings. It will, however, be evident that variousmodifications and changes may be made thereto, and additionalembodiments may be implemented, without departing from the broader scopeof the disclosed embodiments as set forth in the claims that follow.

Further, other embodiments will be apparent to those skilled in the artfrom consideration of the specification and practice of one or moreembodiments of the present disclosure. It is intended, therefore, thatthis disclosure and the examples herein be considered as exemplary only,with a true scope and spirit of the disclosed embodiments beingindicated by the following listing of exemplary claims.

1-20. (canceled)
 21. An apparatus, comprising: a communications unit; a memory storing instructions; and; at least one processor coupled to the communications unit and to the memory, the at least one processor being configured to execute the instructions to: obtain contextual information identifying a first type of data, the first data type being responsive to an inquiry associated with messaging data; based on at least an established consistency between the first data type and a second type of data maintained at an additional apparatus, determine that the additional apparatus is configured to respond to the inquiry; and transmit, via the communications unit, a first signal to the additional apparatus that includes a digital token and at least a portion of the messaging data, the additional apparatus being configured to validate the digital token and perform operations that respond to the inquiry based on the portion of the messaging data.
 22. The apparatus of claim 21, wherein: the inquiry comprises a natural language query; and the at least one processor is further configured to execute the instructions to obtain the messaging data and to generate at least a portion of the contextual information based on an application of natural language processing to the messaging data.
 23. The apparatus of claim 21, wherein: the at least one processor is further configured to execute the instructions to receive, via the communications unit, a second signal from a device during a first communication session, the second signal comprising the messaging data; and the additional apparatus is further configured to generate a response to the inquiry based on the portion of the messaging data and to transmit the response to the device during a second communications session.
 24. The apparatus of claim 23, wherein; a first application program executed by the device generates the messaging data, the messaging data comprising (i) identification data that identifies at least one of the device or the first application program and (ii) query data that characterizes the inquiry; the at least one processor is further configured to execute the instructions to establish the first communications session with the executed first application program; and the executed first application program causes the device to present at least a portion of the response within a digital interface associated with the first communications session.
 25. The apparatus of claim 24, wherein the first signal comprises information that causes a second application program executed by the additional apparatus to establish the second communication session between the executed first application program and a third application program executed by the at least one processor of the apparatus, generate the response based on the portion of the messaging data, and transmit the response to the device during the second communications session.
 26. The apparatus of claim 23, wherein: the messaging data comprises (i) identification data associated with the device and (ii) query data that specifies the inquiry; the at least processor is further configured to execute the instructions to identify a relationship between the apparatus and the additional apparatus based on one or more elements of relationship data maintained within the memory; the digital token comprises an authentication token indicative of a prior authentication of at least one of the device or a user of the device during the first communications session; and the at least one processor is further configured to execute the instructions to, in response to the identified relationship, generate the authentication token based on at least a portion of the identification data or the query data.
 27. The apparatus of claim 23, wherein: the messaging data comprises (i) identification data associated with the device and (ii) query data that specifies the inquiry; and the at least processor is further configured to execute the instructions to: determine that the additional apparatus is unrelated to the apparatus based on one or more elements of relationship data maintained within the memory; transmit, via the communications unit, a second signal to the additional apparatus, the second signal comprising a portion of at least one of the identification data or the query data, and the additional apparatus being further configured to perform operations that authenticate at least one of the device or a user of the device, and that generate the digital token in response to the authentication; and receive, via the communications unit, a third signal that includes the digital token.
 28. The apparatus of claim 23, wherein: the messaging data comprises (i) identification data associated with the device and (ii) query data that specifies the inquiry; the first signal further comprises a portion of the identification data or the query data; and the additional apparatus is further configured to load, from an additional memory, elements of response data consistent with the portion of the identification data or the query data, generate a response to the inquiry based on the elements of response data, and transmit the response to the device, the device being configured to present at least a portion of the response within digital interface associated with the first communications session.
 29. The apparatus of claim 21, wherein the at least one processor is further configured to execute the instructions to determine that the additional apparatus is configured to respond to the inquiry based on (i) the established consistency between the first data type and a second data type maintained at the additional apparatus and (ii) an established inconsistency between the first data type and a third type of data maintained within the memory.
 30. The apparatus of claim 29, wherein the at least one processor is further configured to execute the instructions to establish that the first data type is consistent with the second data type and to establish that the first data type is inconsistent with the third data type.
 31. The apparatus of claim 29, wherein the at least one processor is further configured to execute the instructions to: load, from the memory, capability data that characterizes the second data type and the third data type; and based on the capability data, establish that the first data type is consistent with the second data type to establish that the first data type is inconsistent with the third data type
 32. A computer-implemented method, comprising: obtaining, using at least one processor, contextual information identifying a first type of data, the first data type being responsive to an inquiry associated with messaging data; based on at least an established consistency between the first data type and a second type of data maintained at an additional apparatus, determining, using the at least one processor, that the additional apparatus is configured to respond to the inquiry; and transmitting, using the at least one processor, a first signal to the additional apparatus that includes a digital token and at least a portion of the messaging data, the additional apparatus being configured to validate the digital token and perform operations that respond to the inquiry based on the portion of the messaging data.
 33. The computer-implemented method of claim 32, wherein: the inquiry comprises a natural language query; and the computer-implemented method further comprises, using the at least one processor, obtaining the messaging data and generating at least a portion of the contextual information based on an application of natural language processing to the messaging data.
 34. The computer-implemented method of claim 32, wherein: the computer-implemented method further comprises receiving, using the at least one processor, a second signal from a device during a first communication session, the second signal comprising the messaging data; and the additional apparatus is further configured to generate a response to the inquiry based on the portion of the messaging data and to transmit the response to the device during a second communications session.
 35. The computer-implemented method of claim 32, wherein the determining comprises determining that the additional apparatus is configured to respond to the inquiry based on (i) the established consistency between the first data type and a second data type maintained at an additional apparatus and (ii) an established inconsistency between the first data type and a third type of data maintained within the memory.
 36. An apparatus, comprising: a communications unit; a memory storing instructions; and; at least one processor coupled to the communications unit and to the memory, the at least one processor being configured to execute the instructions to: receive a first signal from a computing system via the communications unit, the first signal comprising a digital token and session data characterizing a first communications session between a device and the computing system; based on a validation of the digital token, establish a second communication session between the device, the computing system, and the apparatus in accordance with a portion of the session data; and transmit, via the communications unit, a second signal that includes interface data associated with the second communications session to the device, the device being configured to present the interface data within a portion of a digital interface associated with the first communications session.
 37. The apparatus of claim 36, wherein the at least one processor is further configured to execute the instructions to generate the interface data.
 38. The apparatus of claim 36, wherein the session data comprises identification data that identifies the device, and the at least one processor is further configured to execute the instructions to transmit the second signal to the device based on the identification data.
 39. The apparatus of claim 36, wherein: the session data comprises query data characterizing an inquiry associated with the first communications session; and the at least one processor is further configured to execute the instructions to: obtain contextual information that characterizes the inquiry, the contextual information identifying a first type of data that is responsive to the inquiry, the first data type being inconsistent with a second type of data maintained at the computing system; and perform operations that respond to the inquiry based on the query data and the contextual information.
 40. The apparatus of claim 39, wherein the at least one processor is further configured to execute the instructions to: load, from the memory, elements of response data that are consistent with the first data type and responsive to the inquiry; generate a response to the inquiry based on at least one of the elements of response data; and transmit, via the communications unit, a third signal to the device that includes the response, the response comprising one or more of the elements of response data, and the device being configured to present at least a portion of the response within the digital interface associated with the first communications session. 